DataCamp offers several interactive courses related to R Programming. While much of it is review, it is always helpful to see other perspectives on material. As well, DataCamp has some interesting materials on packages that I want to learn better (ggplot2, dplyr, ggvis, etc.). This document summarizes a few key insights from:
An additional document will be maintained for several of the more statistical areas of the Data Camp offering, as well as for the few courses offered in Python.
There are a few nuggest from within these beginning modules, including:
Below is some sample code showing examples for the generic statements:
# Factors
xRaw = c("High", "High", "Low", "Low", "Medium", "Very High", "Low")
xFactorNon = factor(xRaw, levels=c("Low", "Medium", "High", "Very High"))
xFactorNon
## [1] High High Low Low Medium Very High Low
## Levels: Low Medium High Very High
xFactorNon[xFactorNon == "High"] > xFactorNon[xFactorNon == "Low"][1]
## Warning in Ops.factor(xFactorNon[xFactorNon == "High"],
## xFactorNon[xFactorNon == : '>' not meaningful for factors
## [1] NA NA
xFactorOrder = factor(xRaw, ordered=TRUE, levels=c("Low", "Medium", "High", "Very High"))
xFactorOrder
## [1] High High Low Low Medium Very High Low
## Levels: Low < Medium < High < Very High
xFactorOrder[xFactorOrder == "High"] > xFactorOrder[xFactorOrder == "Low"][1]
## [1] TRUE TRUE
# Subsets
data(mtcars)
subset(mtcars, mpg>=25)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
identical(subset(mtcars, mpg>=25), mtcars[mtcars$mpg>=25, ])
## [1] TRUE
subset(mtcars, mpg>25, select=c("mpg", "cyl", "disp"))
## mpg cyl disp
## Fiat 128 32.4 4 78.7
## Honda Civic 30.4 4 75.7
## Toyota Corolla 33.9 4 71.1
## Fiat X1-9 27.3 4 79.0
## Porsche 914-2 26.0 4 120.3
## Lotus Europa 30.4 4 95.1
# & and && (same as | and ||)
compA <- c(2, 3, 4, 1, 2, 3)
compB <- c(1, 2, 3, 4, 5, 6)
(compA > compB) & (compA + compB < 6)
## [1] TRUE TRUE FALSE FALSE FALSE FALSE
(compA > compB) | (compA + compB < 6)
## [1] TRUE TRUE TRUE TRUE FALSE FALSE
(compA > compB) && (compA + compB < 6)
## [1] TRUE
(compA > compB) || (compA + compB < 6)
## [1] TRUE
# Loops and cat()
# for (a in b) {
# do stuff
# if (exitCond) { break }
# if (nextCond) { next }
# do some more stuff
# }
for (myVal in compA*compB) {
print(paste0("myVal is: ", myVal))
if ((myVal %% 3) == 0) { cat("Divisible by 3, not happy about that\n\n"); next }
print("That is not divisible by 3")
if ((myVal %% 5) == 0) { cat("Exiting due to divisible by 5 but not divisible by 3\n\n"); break }
cat("Onwards and upwards\n\n")
}
## [1] "myVal is: 2"
## [1] "That is not divisible by 3"
## Onwards and upwards
##
## [1] "myVal is: 6"
## Divisible by 3, not happy about that
##
## [1] "myVal is: 12"
## Divisible by 3, not happy about that
##
## [1] "myVal is: 4"
## [1] "That is not divisible by 3"
## Onwards and upwards
##
## [1] "myVal is: 10"
## [1] "That is not divisible by 3"
## Exiting due to divisible by 5 but not divisible by 3
# args() and search()
args(plot.default)
## function (x, y = NULL, type = "p", xlim = NULL, ylim = NULL,
## log = "", main = NULL, sub = NULL, xlab = NULL, ylab = NULL,
## ann = par("ann"), axes = TRUE, frame.plot = axes, panel.first = NULL,
## panel.last = NULL, asp = NA, ...)
## NULL
search()
## [1] ".GlobalEnv" "package:stats" "package:graphics"
## [4] "package:grDevices" "package:utils" "package:datasets"
## [7] "package:methods" "Autoloads" "package:base"
# unique()
compA
## [1] 2 3 4 1 2 3
unique(compA)
## [1] 2 3 4 1
# unlist()
listA <- as.list(compA)
unlist(listA)
## [1] 2 3 4 1 2 3
identical(compA, unlist(listA))
## [1] TRUE
# sort()
sort(mtcars$mpg)
## [1] 10.4 10.4 13.3 14.3 14.7 15.0 15.2 15.2 15.5 15.8 16.4 17.3 17.8 18.1
## [15] 18.7 19.2 19.2 19.7 21.0 21.0 21.4 21.4 21.5 22.8 22.8 24.4 26.0 27.3
## [29] 30.4 30.4 32.4 33.9
sort(mtcars$mpg, decreasing=TRUE)
## [1] 33.9 32.4 30.4 30.4 27.3 26.0 24.4 22.8 22.8 21.5 21.4 21.4 21.0 21.0
## [15] 19.7 19.2 19.2 18.7 18.1 17.8 17.3 16.4 15.8 15.5 15.2 15.2 15.0 14.7
## [29] 14.3 13.3 10.4 10.4
# rep()
rep(1:6, times=2) # 1:6 followed by 1:6
## [1] 1 2 3 4 5 6 1 2 3 4 5 6
rep(1:6, each=2) # 1 1 2 2 3 3 4 4 5 5 6 6
## [1] 1 1 2 2 3 3 4 4 5 5 6 6
rep(1:6, times=2, each=3) # 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 repeated twice (each comes first)
## [1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6
## [36] 6
rep(1:6, times=6:1) # 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 5 5 6
## [1] 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 5 5 6
# append()
myWords <- c("The", "cat", "in", "the", "hat")
paste(append(myWords, c("is", "fun", "to", "read")), collapse=" ")
## [1] "The cat in the hat is fun to read"
paste(append(myWords, "funny", 4), collapse=" ")
## [1] "The cat in the funny hat"
# grep("//1")
sampMsg <- "This is from myname@subdomain.mydomain.com again"
gsub("(^.*\\w*[a-zA-Z0-9]+@)([a-zA-Z0-9]+\\.[a-zA-Z0-9.]+)(.*$)", "\\1", sampMsg)
## [1] "This is from myname@"
gsub("(^.*\\w*[a-zA-Z0-9]+@)([a-zA-Z0-9]+\\.[a-zA-Z0-9.]+)(.*$)", "\\2", sampMsg)
## [1] "subdomain.mydomain.com"
gsub("(^.*\\w*[a-zA-Z0-9]+@)([a-zA-Z0-9]+\\.[a-zA-Z0-9.]+)(.*$)", "\\3", sampMsg)
## [1] " again"
# rev()
compA
## [1] 2 3 4 1 2 3
rev(compA)
## [1] 3 2 1 4 3 2
Below is some sample code showing examples for the apply statements:
# lapply
args(lapply)
## function (X, FUN, ...)
## NULL
lapply(1:5, FUN=sqrt)
## [[1]]
## [1] 1
##
## [[2]]
## [1] 1.414214
##
## [[3]]
## [1] 1.732051
##
## [[4]]
## [1] 2
##
## [[5]]
## [1] 2.236068
lapply(1:5, FUN=function(x, y=2) { c(x=x, y=y, pow=x^y) }, y=3)
## [[1]]
## x y pow
## 1 3 1
##
## [[2]]
## x y pow
## 2 3 8
##
## [[3]]
## x y pow
## 3 3 27
##
## [[4]]
## x y pow
## 4 3 64
##
## [[5]]
## x y pow
## 5 3 125
lapply(1:5, FUN=function(x, y=2) { if (x <= 3) {c(x=x, y=y, pow=x^y) } else { c(pow=x^y) } }, y=3)
## [[1]]
## x y pow
## 1 3 1
##
## [[2]]
## x y pow
## 2 3 8
##
## [[3]]
## x y pow
## 3 3 27
##
## [[4]]
## pow
## 64
##
## [[5]]
## pow
## 125
# sapply (defaults to returning a named vector/array if possible; is lapply otherwise)
args(sapply)
## function (X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)
## NULL
args(simplify2array)
## function (x, higher = TRUE)
## NULL
sapply(1:5, FUN=sqrt)
## [1] 1.000000 1.414214 1.732051 2.000000 2.236068
sapply(1:5, FUN=function(x, y=2) { c(x=x, y=y, pow=x^y) }, y=3)
## [,1] [,2] [,3] [,4] [,5]
## x 1 2 3 4 5
## y 3 3 3 3 3
## pow 1 8 27 64 125
sapply(1:5, FUN=function(x, y=2) { if (x <= 3) {c(x=x, y=y, pow=x^y) } else { c(pow=x^y) } }, y=3)
## [[1]]
## x y pow
## 1 3 1
##
## [[2]]
## x y pow
## 2 3 8
##
## [[3]]
## x y pow
## 3 3 27
##
## [[4]]
## pow
## 64
##
## [[5]]
## pow
## 125
# vapply (tells sapply exactly what should be returned; errors out otherwise)
args(vapply)
## function (X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE)
## NULL
vapply(1:5, FUN=sqrt, FUN.VALUE=numeric(1))
## [1] 1.000000 1.414214 1.732051 2.000000 2.236068
vapply(1:5, FUN=function(x, y=2) { c(x=x, y=y, pow=x^y) }, FUN.VALUE=numeric(3), y=3)
## [,1] [,2] [,3] [,4] [,5]
## x 1 2 3 4 5
## y 3 3 3 3 3
## pow 1 8 27 64 125
Below is some sample code for handing dates and times in R:
Sys.Date()
## [1] "2017-01-27"
Sys.time()
## [1] "2017-01-27 08:01:39 CST"
args(strptime)
## function (x, format, tz = "")
## NULL
rightNow <- as.POSIXct(Sys.time())
format(rightNow, "%Y**%M-%d %H hours and %M minutes", usetz=TRUE)
## [1] "2017**01-27 08 hours and 01 minutes CST"
lastChristmasNoon <- as.POSIXct("2015-12-25 12:00:00", format="%Y-%m-%d %X")
rightNow - lastChristmasNoon
## Time difference of 398.8345 days
nextUMHomeGame <- as.POSIXct("16/SEP/3 12:00:00", format="%y/%b/%d %H:%M:%S", tz="America/Detroit")
nextUMHomeGame - rightNow
## Time difference of -145.9178 days
# Time zones available in R
OlsonNames()
## [1] "Africa/Abidjan" "Africa/Accra"
## [3] "Africa/Addis_Ababa" "Africa/Algiers"
## [5] "Africa/Asmara" "Africa/Asmera"
## [7] "Africa/Bamako" "Africa/Bangui"
## [9] "Africa/Banjul" "Africa/Bissau"
## [11] "Africa/Blantyre" "Africa/Brazzaville"
## [13] "Africa/Bujumbura" "Africa/Cairo"
## [15] "Africa/Casablanca" "Africa/Ceuta"
## [17] "Africa/Conakry" "Africa/Dakar"
## [19] "Africa/Dar_es_Salaam" "Africa/Djibouti"
## [21] "Africa/Douala" "Africa/El_Aaiun"
## [23] "Africa/Freetown" "Africa/Gaborone"
## [25] "Africa/Harare" "Africa/Johannesburg"
## [27] "Africa/Juba" "Africa/Kampala"
## [29] "Africa/Khartoum" "Africa/Kigali"
## [31] "Africa/Kinshasa" "Africa/Lagos"
## [33] "Africa/Libreville" "Africa/Lome"
## [35] "Africa/Luanda" "Africa/Lubumbashi"
## [37] "Africa/Lusaka" "Africa/Malabo"
## [39] "Africa/Maputo" "Africa/Maseru"
## [41] "Africa/Mbabane" "Africa/Mogadishu"
## [43] "Africa/Monrovia" "Africa/Nairobi"
## [45] "Africa/Ndjamena" "Africa/Niamey"
## [47] "Africa/Nouakchott" "Africa/Ouagadougou"
## [49] "Africa/Porto-Novo" "Africa/Sao_Tome"
## [51] "Africa/Timbuktu" "Africa/Tripoli"
## [53] "Africa/Tunis" "Africa/Windhoek"
## [55] "America/Adak" "America/Anchorage"
## [57] "America/Anguilla" "America/Antigua"
## [59] "America/Araguaina" "America/Argentina/Buenos_Aires"
## [61] "America/Argentina/Catamarca" "America/Argentina/ComodRivadavia"
## [63] "America/Argentina/Cordoba" "America/Argentina/Jujuy"
## [65] "America/Argentina/La_Rioja" "America/Argentina/Mendoza"
## [67] "America/Argentina/Rio_Gallegos" "America/Argentina/Salta"
## [69] "America/Argentina/San_Juan" "America/Argentina/San_Luis"
## [71] "America/Argentina/Tucuman" "America/Argentina/Ushuaia"
## [73] "America/Aruba" "America/Asuncion"
## [75] "America/Atikokan" "America/Atka"
## [77] "America/Bahia" "America/Bahia_Banderas"
## [79] "America/Barbados" "America/Belem"
## [81] "America/Belize" "America/Blanc-Sablon"
## [83] "America/Boa_Vista" "America/Bogota"
## [85] "America/Boise" "America/Buenos_Aires"
## [87] "America/Cambridge_Bay" "America/Campo_Grande"
## [89] "America/Cancun" "America/Caracas"
## [91] "America/Catamarca" "America/Cayenne"
## [93] "America/Cayman" "America/Chicago"
## [95] "America/Chihuahua" "America/Coral_Harbour"
## [97] "America/Cordoba" "America/Costa_Rica"
## [99] "America/Creston" "America/Cuiaba"
## [101] "America/Curacao" "America/Danmarkshavn"
## [103] "America/Dawson" "America/Dawson_Creek"
## [105] "America/Denver" "America/Detroit"
## [107] "America/Dominica" "America/Edmonton"
## [109] "America/Eirunepe" "America/El_Salvador"
## [111] "America/Ensenada" "America/Fort_Nelson"
## [113] "America/Fort_Wayne" "America/Fortaleza"
## [115] "America/Glace_Bay" "America/Godthab"
## [117] "America/Goose_Bay" "America/Grand_Turk"
## [119] "America/Grenada" "America/Guadeloupe"
## [121] "America/Guatemala" "America/Guayaquil"
## [123] "America/Guyana" "America/Halifax"
## [125] "America/Havana" "America/Hermosillo"
## [127] "America/Indiana/Indianapolis" "America/Indiana/Knox"
## [129] "America/Indiana/Marengo" "America/Indiana/Petersburg"
## [131] "America/Indiana/Tell_City" "America/Indiana/Vevay"
## [133] "America/Indiana/Vincennes" "America/Indiana/Winamac"
## [135] "America/Indianapolis" "America/Inuvik"
## [137] "America/Iqaluit" "America/Jamaica"
## [139] "America/Jujuy" "America/Juneau"
## [141] "America/Kentucky/Louisville" "America/Kentucky/Monticello"
## [143] "America/Knox_IN" "America/Kralendijk"
## [145] "America/La_Paz" "America/Lima"
## [147] "America/Los_Angeles" "America/Louisville"
## [149] "America/Lower_Princes" "America/Maceio"
## [151] "America/Managua" "America/Manaus"
## [153] "America/Marigot" "America/Martinique"
## [155] "America/Matamoros" "America/Mazatlan"
## [157] "America/Mendoza" "America/Menominee"
## [159] "America/Merida" "America/Metlakatla"
## [161] "America/Mexico_City" "America/Miquelon"
## [163] "America/Moncton" "America/Monterrey"
## [165] "America/Montevideo" "America/Montreal"
## [167] "America/Montserrat" "America/Nassau"
## [169] "America/New_York" "America/Nipigon"
## [171] "America/Nome" "America/Noronha"
## [173] "America/North_Dakota/Beulah" "America/North_Dakota/Center"
## [175] "America/North_Dakota/New_Salem" "America/Ojinaga"
## [177] "America/Panama" "America/Pangnirtung"
## [179] "America/Paramaribo" "America/Phoenix"
## [181] "America/Port-au-Prince" "America/Port_of_Spain"
## [183] "America/Porto_Acre" "America/Porto_Velho"
## [185] "America/Puerto_Rico" "America/Rainy_River"
## [187] "America/Rankin_Inlet" "America/Recife"
## [189] "America/Regina" "America/Resolute"
## [191] "America/Rio_Branco" "America/Rosario"
## [193] "America/Santa_Isabel" "America/Santarem"
## [195] "America/Santiago" "America/Santo_Domingo"
## [197] "America/Sao_Paulo" "America/Scoresbysund"
## [199] "America/Shiprock" "America/Sitka"
## [201] "America/St_Barthelemy" "America/St_Johns"
## [203] "America/St_Kitts" "America/St_Lucia"
## [205] "America/St_Thomas" "America/St_Vincent"
## [207] "America/Swift_Current" "America/Tegucigalpa"
## [209] "America/Thule" "America/Thunder_Bay"
## [211] "America/Tijuana" "America/Toronto"
## [213] "America/Tortola" "America/Vancouver"
## [215] "America/Virgin" "America/Whitehorse"
## [217] "America/Winnipeg" "America/Yakutat"
## [219] "America/Yellowknife" "Antarctica/Casey"
## [221] "Antarctica/Davis" "Antarctica/DumontDUrville"
## [223] "Antarctica/Macquarie" "Antarctica/Mawson"
## [225] "Antarctica/McMurdo" "Antarctica/Palmer"
## [227] "Antarctica/Rothera" "Antarctica/South_Pole"
## [229] "Antarctica/Syowa" "Antarctica/Troll"
## [231] "Antarctica/Vostok" "Arctic/Longyearbyen"
## [233] "Asia/Aden" "Asia/Almaty"
## [235] "Asia/Amman" "Asia/Anadyr"
## [237] "Asia/Aqtau" "Asia/Aqtobe"
## [239] "Asia/Ashgabat" "Asia/Ashkhabad"
## [241] "Asia/Baghdad" "Asia/Bahrain"
## [243] "Asia/Baku" "Asia/Bangkok"
## [245] "Asia/Beirut" "Asia/Bishkek"
## [247] "Asia/Brunei" "Asia/Calcutta"
## [249] "Asia/Chita" "Asia/Choibalsan"
## [251] "Asia/Chongqing" "Asia/Chungking"
## [253] "Asia/Colombo" "Asia/Dacca"
## [255] "Asia/Damascus" "Asia/Dhaka"
## [257] "Asia/Dili" "Asia/Dubai"
## [259] "Asia/Dushanbe" "Asia/Gaza"
## [261] "Asia/Harbin" "Asia/Hebron"
## [263] "Asia/Ho_Chi_Minh" "Asia/Hong_Kong"
## [265] "Asia/Hovd" "Asia/Irkutsk"
## [267] "Asia/Istanbul" "Asia/Jakarta"
## [269] "Asia/Jayapura" "Asia/Jerusalem"
## [271] "Asia/Kabul" "Asia/Kamchatka"
## [273] "Asia/Karachi" "Asia/Kashgar"
## [275] "Asia/Kathmandu" "Asia/Katmandu"
## [277] "Asia/Khandyga" "Asia/Kolkata"
## [279] "Asia/Krasnoyarsk" "Asia/Kuala_Lumpur"
## [281] "Asia/Kuching" "Asia/Kuwait"
## [283] "Asia/Macao" "Asia/Macau"
## [285] "Asia/Magadan" "Asia/Makassar"
## [287] "Asia/Manila" "Asia/Muscat"
## [289] "Asia/Nicosia" "Asia/Novokuznetsk"
## [291] "Asia/Novosibirsk" "Asia/Omsk"
## [293] "Asia/Oral" "Asia/Phnom_Penh"
## [295] "Asia/Pontianak" "Asia/Pyongyang"
## [297] "Asia/Qatar" "Asia/Qyzylorda"
## [299] "Asia/Rangoon" "Asia/Riyadh"
## [301] "Asia/Saigon" "Asia/Sakhalin"
## [303] "Asia/Samarkand" "Asia/Seoul"
## [305] "Asia/Shanghai" "Asia/Singapore"
## [307] "Asia/Srednekolymsk" "Asia/Taipei"
## [309] "Asia/Tashkent" "Asia/Tbilisi"
## [311] "Asia/Tehran" "Asia/Tel_Aviv"
## [313] "Asia/Thimbu" "Asia/Thimphu"
## [315] "Asia/Tokyo" "Asia/Ujung_Pandang"
## [317] "Asia/Ulaanbaatar" "Asia/Ulan_Bator"
## [319] "Asia/Urumqi" "Asia/Ust-Nera"
## [321] "Asia/Vientiane" "Asia/Vladivostok"
## [323] "Asia/Yakutsk" "Asia/Yekaterinburg"
## [325] "Asia/Yerevan" "Atlantic/Azores"
## [327] "Atlantic/Bermuda" "Atlantic/Canary"
## [329] "Atlantic/Cape_Verde" "Atlantic/Faeroe"
## [331] "Atlantic/Faroe" "Atlantic/Jan_Mayen"
## [333] "Atlantic/Madeira" "Atlantic/Reykjavik"
## [335] "Atlantic/South_Georgia" "Atlantic/St_Helena"
## [337] "Atlantic/Stanley" "Australia/ACT"
## [339] "Australia/Adelaide" "Australia/Brisbane"
## [341] "Australia/Broken_Hill" "Australia/Canberra"
## [343] "Australia/Currie" "Australia/Darwin"
## [345] "Australia/Eucla" "Australia/Hobart"
## [347] "Australia/LHI" "Australia/Lindeman"
## [349] "Australia/Lord_Howe" "Australia/Melbourne"
## [351] "Australia/North" "Australia/NSW"
## [353] "Australia/Perth" "Australia/Queensland"
## [355] "Australia/South" "Australia/Sydney"
## [357] "Australia/Tasmania" "Australia/Victoria"
## [359] "Australia/West" "Australia/Yancowinna"
## [361] "Brazil/Acre" "Brazil/DeNoronha"
## [363] "Brazil/East" "Brazil/West"
## [365] "Canada/Atlantic" "Canada/Central"
## [367] "Canada/East-Saskatchewan" "Canada/Eastern"
## [369] "Canada/Mountain" "Canada/Newfoundland"
## [371] "Canada/Pacific" "Canada/Saskatchewan"
## [373] "Canada/Yukon" "CET"
## [375] "Chile/Continental" "Chile/EasterIsland"
## [377] "CST6CDT" "Cuba"
## [379] "EET" "Egypt"
## [381] "Eire" "EST"
## [383] "EST5EDT" "Etc/GMT"
## [385] "Etc/GMT-0" "Etc/GMT-1"
## [387] "Etc/GMT-10" "Etc/GMT-11"
## [389] "Etc/GMT-12" "Etc/GMT-13"
## [391] "Etc/GMT-14" "Etc/GMT-2"
## [393] "Etc/GMT-3" "Etc/GMT-4"
## [395] "Etc/GMT-5" "Etc/GMT-6"
## [397] "Etc/GMT-7" "Etc/GMT-8"
## [399] "Etc/GMT-9" "Etc/GMT+0"
## [401] "Etc/GMT+1" "Etc/GMT+10"
## [403] "Etc/GMT+11" "Etc/GMT+12"
## [405] "Etc/GMT+2" "Etc/GMT+3"
## [407] "Etc/GMT+4" "Etc/GMT+5"
## [409] "Etc/GMT+6" "Etc/GMT+7"
## [411] "Etc/GMT+8" "Etc/GMT+9"
## [413] "Etc/GMT0" "Etc/Greenwich"
## [415] "Etc/UCT" "Etc/Universal"
## [417] "Etc/UTC" "Etc/Zulu"
## [419] "Europe/Amsterdam" "Europe/Andorra"
## [421] "Europe/Athens" "Europe/Belfast"
## [423] "Europe/Belgrade" "Europe/Berlin"
## [425] "Europe/Bratislava" "Europe/Brussels"
## [427] "Europe/Bucharest" "Europe/Budapest"
## [429] "Europe/Busingen" "Europe/Chisinau"
## [431] "Europe/Copenhagen" "Europe/Dublin"
## [433] "Europe/Gibraltar" "Europe/Guernsey"
## [435] "Europe/Helsinki" "Europe/Isle_of_Man"
## [437] "Europe/Istanbul" "Europe/Jersey"
## [439] "Europe/Kaliningrad" "Europe/Kiev"
## [441] "Europe/Lisbon" "Europe/Ljubljana"
## [443] "Europe/London" "Europe/Luxembourg"
## [445] "Europe/Madrid" "Europe/Malta"
## [447] "Europe/Mariehamn" "Europe/Minsk"
## [449] "Europe/Monaco" "Europe/Moscow"
## [451] "Europe/Nicosia" "Europe/Oslo"
## [453] "Europe/Paris" "Europe/Podgorica"
## [455] "Europe/Prague" "Europe/Riga"
## [457] "Europe/Rome" "Europe/Samara"
## [459] "Europe/San_Marino" "Europe/Sarajevo"
## [461] "Europe/Simferopol" "Europe/Skopje"
## [463] "Europe/Sofia" "Europe/Stockholm"
## [465] "Europe/Tallinn" "Europe/Tirane"
## [467] "Europe/Tiraspol" "Europe/Uzhgorod"
## [469] "Europe/Vaduz" "Europe/Vatican"
## [471] "Europe/Vienna" "Europe/Vilnius"
## [473] "Europe/Volgograd" "Europe/Warsaw"
## [475] "Europe/Zagreb" "Europe/Zaporozhye"
## [477] "Europe/Zurich" "GB"
## [479] "GB-Eire" "GMT"
## [481] "GMT-0" "GMT+0"
## [483] "GMT0" "Greenwich"
## [485] "Hongkong" "HST"
## [487] "Iceland" "Indian/Antananarivo"
## [489] "Indian/Chagos" "Indian/Christmas"
## [491] "Indian/Cocos" "Indian/Comoro"
## [493] "Indian/Kerguelen" "Indian/Mahe"
## [495] "Indian/Maldives" "Indian/Mauritius"
## [497] "Indian/Mayotte" "Indian/Reunion"
## [499] "Iran" "Israel"
## [501] "Jamaica" "Japan"
## [503] "Kwajalein" "Libya"
## [505] "MET" "Mexico/BajaNorte"
## [507] "Mexico/BajaSur" "Mexico/General"
## [509] "MST" "MST7MDT"
## [511] "Navajo" "NZ"
## [513] "NZ-CHAT" "Pacific/Apia"
## [515] "Pacific/Auckland" "Pacific/Bougainville"
## [517] "Pacific/Chatham" "Pacific/Chuuk"
## [519] "Pacific/Easter" "Pacific/Efate"
## [521] "Pacific/Enderbury" "Pacific/Fakaofo"
## [523] "Pacific/Fiji" "Pacific/Funafuti"
## [525] "Pacific/Galapagos" "Pacific/Gambier"
## [527] "Pacific/Guadalcanal" "Pacific/Guam"
## [529] "Pacific/Honolulu" "Pacific/Johnston"
## [531] "Pacific/Kiritimati" "Pacific/Kosrae"
## [533] "Pacific/Kwajalein" "Pacific/Majuro"
## [535] "Pacific/Marquesas" "Pacific/Midway"
## [537] "Pacific/Nauru" "Pacific/Niue"
## [539] "Pacific/Norfolk" "Pacific/Noumea"
## [541] "Pacific/Pago_Pago" "Pacific/Palau"
## [543] "Pacific/Pitcairn" "Pacific/Pohnpei"
## [545] "Pacific/Ponape" "Pacific/Port_Moresby"
## [547] "Pacific/Rarotonga" "Pacific/Saipan"
## [549] "Pacific/Samoa" "Pacific/Tahiti"
## [551] "Pacific/Tarawa" "Pacific/Tongatapu"
## [553] "Pacific/Truk" "Pacific/Wake"
## [555] "Pacific/Wallis" "Pacific/Yap"
## [557] "Poland" "Portugal"
## [559] "PRC" "PST8PDT"
## [561] "ROC" "ROK"
## [563] "Singapore" "Turkey"
## [565] "UCT" "Universal"
## [567] "US/Alaska" "US/Aleutian"
## [569] "US/Arizona" "US/Central"
## [571] "US/East-Indiana" "US/Eastern"
## [573] "US/Hawaii" "US/Indiana-Starke"
## [575] "US/Michigan" "US/Mountain"
## [577] "US/Pacific" "US/Pacific-New"
## [579] "US/Samoa" "UTC"
## [581] "VERSION" "W-SU"
## [583] "WET" "Zulu"
# From ?strptime (excerpted)
#
# ** General formats **
# %c Date and time. Locale-specific on output, "%a %b %e %H:%M:%S %Y" on input.
# %F Equivalent to %Y-%m-%d (the ISO 8601 date format).
# %T Equivalent to %H:%M:%S.
# %D Date format such as %m/%d/%y: the C99 standard says it should be that exact format
# %x Date. Locale-specific on output, "%y/%m/%d" on input.
# %X Time. Locale-specific on output, "%H:%M:%S" on input.
#
# ** Key Components **
# %y Year without century (00-99). On input, values 00 to 68 are prefixed by 20 and 69 to 99 by 19
# %Y Year with century
# %m Month as decimal number (01-12).
# %b Abbreviated month name in the current locale on this platform.
# %B Full month name in the current locale.
# %d Day of the month as decimal number (01-31).
# %e Day of the month as decimal number (1-31), with a leading space for a single-digit number.
# %a Abbreviated weekday name in the current locale on this platform.
# %A Full weekday name in the current locale.
# %H Hours as decimal number (00-23)
# %I Hours as decimal number (01-12)
# %M Minute as decimal number (00-59).
# %S Second as integer (00-61), allowing for up to two leap-seconds (but POSIX-compliant implementations will ignore leap seconds).
#
# ** Additional Options **
# %C Century (00-99): the integer part of the year divided by 100.
#
# %g The last two digits of the week-based year (see %V). (Accepted but ignored on input.)
# %G The week-based year (see %V) as a decimal number. (Accepted but ignored on input.)
#
# %h Equivalent to %b.
#
# %j Day of year as decimal number (001-366).
#
# %n Newline on output, arbitrary whitespace on input.
#
# %p AM/PM indicator in the locale. Used in conjunction with %I and not with %H. An empty string in some locales (and the behaviour is undefined if used for input in such a locale). Some platforms accept %P for output, which uses a lower-case version: others will output P.
#
# %r The 12-hour clock time (using the locale's AM or PM). Only defined in some locales.
#
# %R Equivalent to %H:%M.
#
# %t Tab on output, arbitrary whitespace on input.
#
# %u Weekday as a decimal number (1-7, Monday is 1).
#
# %U Week of the year as decimal number (00-53) using Sunday as the first day 1 of the week (and typically with the first Sunday of the year as day 1 of week 1). The US convention.
#
# %V Week of the year as decimal number (01-53) as defined in ISO 8601. If the week (starting on Monday) containing 1 January has four or more days in the new year, then it is considered week 1. Otherwise, it is the last week of the previous year, and the next week is week 1. (Accepted but ignored on input.)
#
# %w Weekday as decimal number (0-6, Sunday is 0).
#
# %W Week of the year as decimal number (00-53) using Monday as the first day of week (and typically with the first Monday of the year as day 1 of week 1). The UK convention.
#
# For input, only years 0:9999 are accepted.
#
# %z Signed offset in hours and minutes from UTC, so -0800 is 8 hours behind UTC. Values up to +1400 are accepted as from R 3.1.1: previous versions only accepted up to +1200. (Standard only for output.)
#
# %Z (Output only.) Time zone abbreviation as a character string (empty if not available). This may not be reliable when a time zone has changed abbreviations over the years.
Additionally, code from several practice examples is added:
set.seed(1608221310)
me <- 89
other_199 <- round(rnorm(199, mean=75.45, sd=11.03), 0)
mean(other_199)
## [1] 75.17588
sd(other_199)
## [1] 11.37711
desMeans <- c(72.275, 76.24, 74.5, 77.695)
desSD <- c(12.31, 11.22, 12.5, 12.53)
prevData <- c(rnorm(200, mean=72.275, sd=12.31),
rnorm(200, mean=76.24, sd=11.22),
rnorm(200, mean=74.5, sd=12.5),
rnorm(200, mean=77.695, sd=12.53)
)
previous_4 <- matrix(data=prevData, ncol=4)
curMeans <- apply(previous_4, 2, FUN=mean)
curSD <- apply(previous_4, 2, FUN=sd)
previous_4 <- t(apply(previous_4, 1, FUN=function(x) { desMeans + (desSD / curSD) * (x - curMeans) } ))
apply(round(previous_4, 0), 2, FUN=mean)
## [1] 72.285 76.245 74.505 77.665
apply(round(previous_4, 0), 2, FUN=sd)
## [1] 12.35097 11.19202 12.49643 12.51744
previous_4 <- round(previous_4, 0)
# Merge me and other_199: my_class
my_class <- c(me, other_199)
# cbind() my_class and previous_4: last_5
last_5 <- cbind(my_class, previous_4)
# Name last_5 appropriately
nms <- paste0("year_", 1:5)
colnames(last_5) <- nms
# Build histogram of my_class
hist(my_class)
# Generate summary of last_5
summary(last_5)
## year_1 year_2 year_3 year_4
## Min. : 46.00 Min. : 43.00 Min. : 38.00 Min. : 42.00
## 1st Qu.: 68.00 1st Qu.: 63.75 1st Qu.: 69.00 1st Qu.: 65.75
## Median : 75.50 Median : 73.00 Median : 76.50 Median : 74.00
## Mean : 75.25 Mean : 72.28 Mean : 76.25 Mean : 74.50
## 3rd Qu.: 83.25 3rd Qu.: 81.00 3rd Qu.: 84.25 3rd Qu.: 82.25
## Max. :108.00 Max. :108.00 Max. :102.00 Max. :113.00
## year_5
## Min. : 38.00
## 1st Qu.: 71.00
## Median : 78.00
## Mean : 77.67
## 3rd Qu.: 86.00
## Max. :117.00
# Build boxplot of last_5
boxplot(last_5)
# How many grades in your class are higher than 75?
sum(my_class > 75)
## [1] 100
# How many students in your class scored strictly higher than you?
sum(my_class > me)
## [1] 17
# What's the proportion of grades below or equal to 64 in the last 5 years?
mean(last_5 <= 64)
## [1] 0.191
# Is your grade greater than 87 and smaller than or equal to 89?
me > 87 & me <= 89
## [1] TRUE
# Which grades in your class are below 60 or above 90?
my_class < 60 | my_class > 90
## [1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [23] TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
## [34] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [45] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [56] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## [78] FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
## [89] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE
## [100] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [111] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [122] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
## [133] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [144] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [155] FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [166] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [177] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [188] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [199] FALSE FALSE
# What's the proportion of grades in your class that is average?
mean(my_class >= 70 & my_class <= 85)
## [1] 0.525
# How many students in the last 5 years had a grade of 80 or 90?
sum(last_5 %in% c(80, 90))
## [1] 44
# Define n_smart
n_smart <- sum(my_class >= 80)
# Code the if-else construct
if (n_smart > 50) {
print("smart class")
} else {
print("rather average")
}
## [1] "smart class"
# Define prop_less
prop_less <- mean(my_class < me)
# Code the control construct
if (prop_less > 0.9) {
print("you're among the best 10 percent")
} else if (prop_less > 0.8) {
print("you're among the best 20 percent")
} else {
print("need more analysis")
}
## [1] "you're among the best 20 percent"
# Embedded control structure: fix the error
if (mean(my_class) < 75) {
if (mean(my_class) > me) {
print("average year, but still smarter than me")
} else {
print("average year, but I'm not that bad")
}
} else {
if (mean(my_class) > me) {
print("smart year, even smarter than me")
} else {
print("smart year, but I am smarter")
}
}
## [1] "smart year, but I am smarter"
# Create top_grades
top_grades <- my_class[my_class >= 85]
# Create worst_grades
worst_grades <- my_class[my_class < 65]
# Write conditional statement
if (length(top_grades) > length(worst_grades)) { print("top grades prevail") }
## [1] "top grades prevail"
Hadley and Charlotte Wickham led a course on writing functions in R. Broadly, the course includes advice on when/how to use functions, as well as specific advice about commands available through library(purrr).
Key pieces of advice include:
John Chambers gave a few useful slogans about functions:
Each function has three components:
Only the LAST evaluated expression is returned. The use of return() is recommended only for early-returns in a special case (for example, when a break() will be called).
Further, functions can be written anonymously on the command line, such as (function (x) {x + 1}) (1:5). A function should only depend on arguments passed to it, not variables from a parent enviornment. Every time the function is called, it receives a clean working environment. Once it finishes, its variables are no longer available unless they were returned (either by default as the last operation, or by way of return()):
# Components of a function
args(rnorm)
## function (n, mean = 0, sd = 1)
## NULL
formals(rnorm)
## $n
##
##
## $mean
## [1] 0
##
## $sd
## [1] 1
body(rnorm)
## .Call(C_rnorm, n, mean, sd)
environment(rnorm)
## <environment: namespace:stats>
# What is passed back
funDummy <- function(x) {
if (x <= 2) {
print("That is too small")
return(3) # This ends the function by convention
}
ceiling(x) # This is the defaulted return() value if nothing happened to prevent the code getting here
}
funDummy(1)
## [1] "That is too small"
## [1] 3
funDummy(5)
## [1] 5
# Anonymous functions
(function (x) {x + 1}) (1:5)
## [1] 2 3 4 5 6
The course includes some insightful discussion of vectors. As it happens, lists and data frames are just special collections of vectors in R. Each column of a data frame is a vector, while each element of a list is either 1) an embedded data frame (which is eventually a vector by way of columns), 2) an embedded list (which is eventually a vector by way of recursion), or 3) an actual vector.
The atomic vectors are of types logical, integer, character, and double; complex and raw are rarer types that are also available. Lists are just recursive vectors, which is to say that lists can contain other lists and can be hetergeneous. To explore vectors, you have:
Note that NULL is the absence of a vector and has length 0. NA is the absence of an element in the vector and has length 1. All math operations with NA return NA; for example NA == NA will return NA.
There are some good tips on extracting element from a list:
# Data types
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
typeof(mtcars) # n.b. that this is technically a "list"
## [1] "list"
length(mtcars)
## [1] 11
# NULL and NA
length(NULL)
## [1] 0
typeof(NULL)
## [1] "NULL"
length(NA)
## [1] 1
typeof(NA)
## [1] "logical"
NULL == NULL
## logical(0)
NULL == NA
## logical(0)
NA == NA
## [1] NA
is.null(NULL)
## [1] TRUE
is.null(NA)
## [1] FALSE
is.na(NULL)
## Warning in is.na(NULL): is.na() applied to non-(list or vector) of type
## 'NULL'
## logical(0)
is.na(NA)
## [1] TRUE
# Extraction
mtcars[["mpg"]][1:5]
## [1] 21.0 21.0 22.8 21.4 18.7
mtcars[[2]][1:5]
## [1] 6 6 4 6 8
mtcars$hp[1:5]
## [1] 110 110 93 110 175
# Relevant lengths
seq_along(mtcars)
## [1] 1 2 3 4 5 6 7 8 9 10 11
x <- data.frame()
seq_along(x)
## integer(0)
length(seq_along(x))
## [1] 0
foo <- function(x) { for (eachCol in seq_along(x)) { print(typeof(x[[eachCol]])) }}
foo(mtcars)
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
## [1] "double"
foo(x) # Note that this does nothing!
data(airquality)
str(airquality)
## 'data.frame': 153 obs. of 6 variables:
## $ Ozone : int 41 36 12 18 NA 28 23 19 8 NA ...
## $ Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...
## $ Wind : num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
## $ Temp : int 67 72 74 62 56 66 65 59 61 69 ...
## $ Month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Day : int 1 2 3 4 5 6 7 8 9 10 ...
foo(airquality)
## [1] "integer"
## [1] "integer"
## [1] "double"
## [1] "integer"
## [1] "integer"
## [1] "integer"
# Range command
mpgRange <- range(mtcars$mpg)
mpgRange
## [1] 10.4 33.9
mpgScale <- (mtcars$mpg - mpgRange[1]) / (mpgRange[2] - mpgRange[1])
summary(mpgScale)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2138 0.3745 0.4124 0.5277 1.0000
The typical arguments in a function use a consistent, simple naming function:
Data arguments should come before detail arguments, and detail arguments should be given reasonable default values. See for example rnorm(n, mean=0, sd=1). The number requested (n) must be specified, but defaults are available for the details (mean and standard deviation).
Functions can be passed as arguments to other functions, which is at the core of functional programming. For example:
do_math <- function(x, fun) { fun(x) }
do_math(1:10, fun=mean)
## [1] 5.5
do_math(1:10, fun=sd)
## [1] 3.02765
The library(purrr) takes advantage of this, and in a type-consistent manner. There are functions for:
The general arguments are .x (a list or an atomic vector) and .f which can be either a function, an anonymous function (formula with ~), or an extractor .x[[.f]]. For example:
library(purrr)
## Warning: package 'purrr' was built under R version 3.2.5
library(RColorBrewer) # Need to have in non-cached chunk for later
data(mtcars)
# Create output as a list
map(.x=mtcars, .f=sum)
## $mpg
## [1] 642.9
##
## $cyl
## [1] 198
##
## $disp
## [1] 7383.1
##
## $hp
## [1] 4694
##
## $drat
## [1] 115.09
##
## $wt
## [1] 102.952
##
## $qsec
## [1] 571.16
##
## $vs
## [1] 14
##
## $am
## [1] 13
##
## $gear
## [1] 118
##
## $carb
## [1] 90
# Create same output as a double
map_dbl(.x=mtcars, .f=sum)
## mpg cyl disp hp drat wt qsec vs
## 642.900 198.000 7383.100 4694.000 115.090 102.952 571.160 14.000
## am gear carb
## 13.000 118.000 90.000
# Create same output as integer
# map_int(.x=mtcars, .f=sum) . . . this would bomb since it is not actually an integere
map_int(.x=mtcars, .f=function(x) { as.integer(round(sum(x), 0)) } )
## mpg cyl disp hp drat wt qsec vs am gear carb
## 643 198 7383 4694 115 103 571 14 13 118 90
# Same thing but using an anonymous function with ~ and .
map_int(.x=mtcars, .f = ~ as.integer(round(sum(.), 0)) )
## mpg cyl disp hp drat wt qsec vs am gear carb
## 643 198 7383 4694 115 103 571 14 13 118 90
# Create a boolean vector
map_lgl(.x=mtcars, .f = ~ ifelse(sum(.) > 200, TRUE, FALSE) )
## mpg cyl disp hp drat wt qsec vs am gear carb
## TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
# Create a character vector
map_chr(.x=mtcars, .f = ~ ifelse(sum(.) > 200, "Large", "Not So Large") )
## mpg cyl disp hp drat
## "Large" "Not So Large" "Large" "Large" "Not So Large"
## wt qsec vs am gear
## "Not So Large" "Large" "Not So Large" "Not So Large" "Not So Large"
## carb
## "Not So Large"
# Use the extractor [pulls the first row]
map_dbl(.x=mtcars, .f=1)
## mpg cyl disp hp drat wt qsec vs am gear
## 21.00 6.00 160.00 110.00 3.90 2.62 16.46 0.00 1.00 4.00
## carb
## 4.00
# Example from help file using chaining
mtcars %>%
split(.$cyl) %>%
map(~ lm(mpg ~ wt, data = .x)) %>%
map(summary) %>%
map_dbl("r.squared")
## 4 6 8
## 0.5086326 0.4645102 0.4229655
# Using sapply
sapply(split(mtcars, mtcars$cyl), FUN=function(.x) { summary(lm(mpg ~ wt, data=.x))$r.squared } )
## 4 6 8
## 0.5086326 0.4645102 0.4229655
# Use the extractor from a list
cylSplit <- split(mtcars, mtcars$cyl)
map(cylSplit, "mpg")
## $`4`
## [1] 22.8 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26.0 30.4 21.4
##
## $`6`
## [1] 21.0 21.0 21.4 18.1 19.2 17.8 19.7
##
## $`8`
## [1] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 13.3 19.2 15.8 15.0
map(cylSplit, "cyl")
## $`4`
## [1] 4 4 4 4 4 4 4 4 4 4 4
##
## $`6`
## [1] 6 6 6 6 6 6 6
##
## $`8`
## [1] 8 8 8 8 8 8 8 8 8 8 8 8 8 8
The purrr library has several additional interesting functions:
Some example code includes:
library(purrr) # Called again for clarity; all these key functions belong to purrr
# safely(.f, otherwise = NULL, quiet = TRUE)
safe_log10 <- safely(log10)
map(list(0, 1, 10, "a"), .f=safe_log10)
## [[1]]
## [[1]]$result
## [1] -Inf
##
## [[1]]$error
## NULL
##
##
## [[2]]
## [[2]]$result
## [1] 0
##
## [[2]]$error
## NULL
##
##
## [[3]]
## [[3]]$result
## [1] 1
##
## [[3]]$error
## NULL
##
##
## [[4]]
## [[4]]$result
## NULL
##
## [[4]]$error
## <simpleError in .f(...): non-numeric argument to mathematical function>
# possibly(.f, otherwise, quiet = TRUE)
poss_log10 <- possibly(log10, otherwise=NaN)
map_dbl(list(0, 1, 10, "a"), .f=poss_log10)
## [1] -Inf 0 1 NaN
# transpose() - note that this can become masked by data.table::transpose() so be careful
purrr::transpose(map(list(0, 1, 10, "a"), .f=safe_log10))
## $result
## $result[[1]]
## [1] -Inf
##
## $result[[2]]
## [1] 0
##
## $result[[3]]
## [1] 1
##
## $result[[4]]
## NULL
##
##
## $error
## $error[[1]]
## NULL
##
## $error[[2]]
## NULL
##
## $error[[3]]
## NULL
##
## $error[[4]]
## <simpleError in .f(...): non-numeric argument to mathematical function>
purrr::transpose(map(list(0, 1, 10, "a"), .f=safe_log10))$result
## [[1]]
## [1] -Inf
##
## [[2]]
## [1] 0
##
## [[3]]
## [1] 1
##
## [[4]]
## NULL
unlist(purrr::transpose(map(list(0, 1, 10, "a"), .f=safe_log10))$result)
## [1] -Inf 0 1
purrr::transpose(map(list(0, 1, 10, "a"), .f=safe_log10))$error
## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL
##
## [[4]]
## <simpleError in .f(...): non-numeric argument to mathematical function>
map_lgl(purrr::transpose(map(list(0, 1, 10, "a"), .f=safe_log10))$error, is.null)
## [1] TRUE TRUE TRUE FALSE
# map2(.x, .y, .f)
map2(list(5, 10, 20), list(1, 2, 3), .f=rnorm) # rnorm(5, 1), rnorm(10, 2), and rnorm(20, 3)
## [[1]]
## [1] 0.41176421 2.00652288 0.06152025 0.46963873 1.15436157
##
## [[2]]
## [1] 0.006821057 2.902712636 1.436150816 1.377836302 2.625075832
## [6] 0.680797806 0.313499192 0.718062969 2.820989906 3.134207742
##
## [[3]]
## [1] 3.3716474 2.9393673 1.8648940 3.2343343 2.1849894 2.0697179 1.0872014
## [8] 3.4970403 3.5769694 3.0999340 1.2033341 0.9839011 2.9820314 1.7116383
## [15] 0.8779558 1.6990118 2.5914013 2.3587803 3.7460957 1.2980312
# pmap(.l, .f)
pmap(list(n=list(5, 10, 20), mean=list(1, 5, 10), sd=list(0.1, 0.5, 0.1)), rnorm)
## [[1]]
## [1] 1.0151570 1.1573287 1.0628581 0.8805484 0.9418430
##
## [[2]]
## [1] 5.032920 4.689799 5.423525 5.265610 4.727383 5.252325 5.166292
## [8] 4.861745 5.135408 4.106679
##
## [[3]]
## [1] 9.854138 10.090939 10.045554 9.970755 10.092487 9.769531 10.140064
## [8] 9.834716 10.196817 10.047367 10.054093 10.006439 10.142002 10.092259
## [15] 10.222459 10.082440 10.067818 9.993884 10.078380 9.936942
# invoke_map(.f, .x, ...)
invoke_map(list(rnorm, runif, rexp), n=5)
## [[1]]
## [1] -0.96707137 0.08207476 1.39498168 0.60287972 -0.15130461
##
## [[2]]
## [1] 0.01087442 0.02980483 0.81443586 0.88438198 0.67976034
##
## [[3]]
## [1] 0.2646751 1.3233260 1.1079261 1.3504952 0.6795524
# walk() is for the side effects of a function
x <- list(1, "\n\ta\n", 3)
x %>% walk(cat)
## 1
## a
## 3
# Chaining is available by way of the %>% operator
pretty_titles <- c("N(0, 1)", "Uniform(0, 1)", "Exponential (rate=1)")
set.seed(1607120947)
x <- invoke_map(list(rnorm, runif, rexp), n=5000)
foo <- function(x) { map(x, .f=summary) }
par(mfrow=c(1, 3))
pwalk(list(x=x, main=pretty_titles), .f=hist, xlab="", col="light blue") %>% map(.f=foo)
## $x
## $x[[1]]
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.711000 -0.637800 -0.000217 0.006543 0.671800 3.633000
##
## $x[[2]]
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0001241 0.2518000 0.5012000 0.5028000 0.7566000 0.9999000
##
## $x[[3]]
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00001 0.29140 0.68340 0.98260 1.37900 8.46300
##
##
## $main
## $main[[1]]
## Length Class Mode
## 1 character character
##
## $main[[2]]
## Length Class Mode
## 1 character character
##
## $main[[3]]
## Length Class Mode
## 1 character character
par(mfrow=c(1, 1))
There are two potentially desirable behaviors with functions:
As a best practice, R functions that will be used for programming (as opposed to interactive command line work) should be written in a robust manner. Three standard problems should be avoided/mitigated:
There are several methods available for throwing errors within an R function:
One example that commonly creates surprises is the [,] operator for extraction. Adding [ , , drop=FALSE] ensures that you will still have what you passed (e.g., a matrix or data frame) rather than conversion of a chunk of data to a vector.
Another common source of error is sapply() which will return a vector when it can and a list otherwise. The map() and map_typ() functions in purrr are designed to be type-stable; if the output is not as expected, they will error out.
Non-standard evaluations take advantage of the existence of something else (e.g., a variable in the parent environment that has not been passed). This can cause confusion and improper results.
Pure functions have the key properties that 1) their output depends only on their inputs, and 2) they do not impact the outside world other than by way of their return value. Specifically, the function should not depend on how the user has configured their global options as shown in options(), nor should it modify those options() settings upon return of control to the parent environment.
A few examples are shown below:
# Throwing errors to stop a function (cannot actually run these!)
# stopifnot(FALSE)
# if (FALSE) { stop("Error: ", call.=FALSE) }
# if (FALSE) { stop("Error: This condition needed to be set as TRUE", call.=FALSE) }
# Behavior of [,] and [,,drop=FALSE]
mtxTest <- matrix(data=1:9, nrow=3, byrow=TRUE)
class(mtxTest)
## [1] "matrix"
mtxTest[1, ]
## [1] 1 2 3
class(mtxTest[1, ])
## [1] "integer"
mtxTest[1, , drop=FALSE]
## [,1] [,2] [,3]
## [1,] 1 2 3
class(mtxTest[1, , drop=FALSE])
## [1] "matrix"
# Behavior of sapply() - may not get what you are expecting
foo <- function(x) { x^2 }
sapply(1:5, FUN=foo)
## [1] 1 4 9 16 25
class(sapply(1:5, FUN=foo))
## [1] "numeric"
sapply(c(1, list(1.5, 2, 2.5), 3, 4, 5), FUN=foo)
## [1] 1.00 2.25 4.00 6.25 9.00 16.00 25.00
class(sapply(c(1, list(1.5, 2, 2.5), 3, 4, 5), FUN=foo))
## [1] "numeric"
sapply(list(1, c(1.5, 2, 2.5), 3, 4, 5), FUN=foo)
## [[1]]
## [1] 1
##
## [[2]]
## [1] 2.25 4.00 6.25
##
## [[3]]
## [1] 9
##
## [[4]]
## [1] 16
##
## [[5]]
## [1] 25
class(sapply(list(1, c(1.5, 2, 2.5), 3, 4, 5), FUN=foo))
## [1] "list"
This was a very enjoyable and instructive course.
This course provides an overview of loading data in to R from five main sources:
At the most basic level, the utlis library easily handles reading most types of flat files:
There are also European equivalents in case the decimal needs to be set as “,” to read in the file:
The file.path() command is a nice way to put together file paths. It is more or less equivalent to paste(, sep=“/”), but with the benefit that sep is machine/operating-system dependent, so it may be easier to use across platforms.
Further, there is the option to use colClasses() to specify the type in each column, with NULL meaning do not import. Abbreviations can be used for these as well:
# colClasses (relevant abbreviations)
R.utils::colClasses("-?cdfilnrzDP")
## [1] "NULL" "NA" "character" "double" "factor"
## [6] "integer" "logical" "numeric" "raw" "complex"
## [11] "Date" "POSIXct"
# file.path example
file.path("..", "myplot.pdf")
## [1] "../myplot.pdf"
# Key documentation for reading flat files
#
# read.table(file, header = FALSE, sep = "", quote = "\"'",
# dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
# row.names, col.names, as.is = !stringsAsFactors,
# na.strings = "NA", colClasses = NA, nrows = -1,
# skip = 0, check.names = TRUE, fill = !blank.lines.skip,
# strip.white = FALSE, blank.lines.skip = TRUE,
# comment.char = "#",
# allowEscapes = FALSE, flush = FALSE,
# stringsAsFactors = default.stringsAsFactors(),
# fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)
#
# read.csv(file, header = TRUE, sep = ",", quote = "\"",
# dec = ".", fill = TRUE, comment.char = "", ...)
#
# read.csv2(file, header = TRUE, sep = ";", quote = "\"",
# dec = ",", fill = TRUE, comment.char = "", ...)
#
# read.delim(file, header = TRUE, sep = "\t", quote = "\"",
# dec = ".", fill = TRUE, comment.char = "", ...)
#
# read.delim2(file, header = TRUE, sep = "\t", quote = "\"",
# dec = ",", fill = TRUE, comment.char = "", ...)
There are also two libraries that can be especially helpful for reading in flat files - readr and data.table.
read_tsv is for tab-separated values
Further, the library(readxl) is handy for loading Excel sheets:
R can also load files from common statistical software such as SAS, STATA, SPSS, and MATLAB/Octave. The packages haven() by Wickham and foreign() by the R core team are two common examples. The R.matlab() allows for reading to/from MATLAB/Octave:
The library(haven) contains wrappers to the ReadStat package, a C library by Evan Miller, for reading files from SAS, STATA, and SPSS:
The library(foreign) can read/write all types of foreign formats, with some caveats:
Finally, the R.matlab() library is available for reading/writing MATLAB/Octave files. Per the help file:
Methods readMat() and writeMat() for reading and writing MAT files. For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
In brief, this package provides a one-directional interface from R to MATLAB, with communication taking place via a TCP/IP connection and with data transferred either through another connection or via the file system. On the MATLAB side, the TCP/IP connection is handled by a small Java add-on.
The methods for reading and writing MAT files are stable. The R to MATLAB interface, that is the Matlab class, is less prioritized and should be considered a beta version.
Relational databases in R (DBMS tend to use SQL for queries), including libraries:
Conventions are specified in DBI; see library(DBI):
Create the connection as “con” (or whatever) and then use that elsewhere:
When finished, dbDisconnect(con) as a courtesy so as to not tie up resources.
SQL queries from inside R - per previous, library(DBI) and then create the connection “con”:
For example, using “./SQLforDataCampRMD_v01.db”, run a few SQL commands:
# uses libraries DBI for the connection and RSQLite to interface with SQLite Browser on my machine
con <- DBI::dbConnect(RSQLite::SQLite(), "SQLforDataCampRMD_v01.db")
# List the tables, and drop dummy if it already exists
DBI::dbListTables(con)
## [1] "dummy"
DBI::dbGetQuery(con, "DROP TABLE IF EXISTS dummy")
# Create blank table
DBI::dbListTables(con)
## character(0)
DBI::dbGetQuery(con, "CREATE TABLE IF NOT EXISTS dummy (id PRIMARY KEY, name CHAR)")
DBI::dbGetQuery(con, "INSERT OR IGNORE INTO dummy (id, name) VALUES (1, 'Amy')")
DBI::dbGetQuery(con, "INSERT OR IGNORE INTO dummy (id, name) VALUES (2, 'Bill')")
DBI::dbGetQuery(con, "INSERT OR IGNORE INTO dummy (id, name) VALUES (2, 'Jen')") # Should do nothing
DBI::dbGetQuery(con, "SELECT * FROM dummy")
## id name
## 1 1 Amy
## 2 2 Bill
DBI::dbListTables(con)
## [1] "dummy"
# Can continue passing SQL commands back and forth as needed
# Close the connection
DBI::dbDisconnect(con)
## [1] TRUE
Many of the R read-in libraries already work well with web data. For example, read.csv(“mywebsite.com”, stringAsFactors=FALSE) will read a CSV right off the internet. Further, there are options for:
The jsonlite library is good for working with JSON:
Prettify adds indentation to a JSON string; minify removes all indentation/whitespace:
jsonLoc <- file.path("../../..", "PythonDirectory", "UMModule04", "roster_data.json")
jsonData <- jsonlite::fromJSON(jsonLoc)
str(jsonData)
## chr [1:379, 1:3] "Calvin" "Wilson" "Emi" "Rosina" "Sylvie" ...
head(jsonData)
## [,1] [,2] [,3]
## [1,] "Calvin" "si110" "1"
## [2,] "Wilson" "si110" "0"
## [3,] "Emi" "si110" "0"
## [4,] "Rosina" "si110" "0"
## [5,] "Sylvie" "si110" "0"
## [6,] "Katarzyna" "si110" "0"
The general analysis pipeline is Collect -> Clean -> Analyze -> Report. Cleaning is needed so the raw data can work with more traditional tools (e.g., packages in Python or R). 50% - 80% of time is spent in the Collect/Clean realm, even though this is not the most exciting (and thus taught) part of data analysis. There are generally three stages of data cleaning: Explore -> Tidy -> Prepare
Exploring the Data:
Viewing the Data:
Tidy data - Wickham 2014, Principles of Tidy Data:
The principles of tidy data can be implemented using library(tidyr):
Common symptoms of messy data include:
Example code includes:
# tidyr::gather()
stocks <- data.frame(time = as.Date('2009-01-01') + 0:4,
X = rnorm(5, 0, 1), Y = rnorm(5, 0, 2), Z = rnorm(5, 0, 4)
)
stocks
## time X Y Z
## 1 2009-01-01 1.64736472 -0.1020457 -8.074672
## 2 2009-01-02 0.32981671 -0.2377234 7.617473
## 3 2009-01-03 0.05010405 -0.7091054 -9.770047
## 4 2009-01-04 0.41187479 1.1899260 -1.655071
## 5 2009-01-05 -2.20625659 -1.1299452 1.615068
# will create new columns stock (each of X, Y, Z) and price (the values that had been in X, Y, and Z),
# while not gathering the time variable; final table will be time-stock-price
stockGather <- tidyr::gather(stocks, stock, price, -time)
stockGather
## time stock price
## 1 2009-01-01 X 1.64736472
## 2 2009-01-02 X 0.32981671
## 3 2009-01-03 X 0.05010405
## 4 2009-01-04 X 0.41187479
## 5 2009-01-05 X -2.20625659
## 6 2009-01-01 Y -0.10204566
## 7 2009-01-02 Y -0.23772338
## 8 2009-01-03 Y -0.70910541
## 9 2009-01-04 Y 1.18992602
## 10 2009-01-05 Y -1.12994523
## 11 2009-01-01 Z -8.07467238
## 12 2009-01-02 Z 7.61747283
## 13 2009-01-03 Z -9.77004663
## 14 2009-01-04 Z -1.65507149
## 15 2009-01-05 Z 1.61506772
# tidyr::spread()
tidyr::spread(stockGather, stock, price)
## time X Y Z
## 1 2009-01-01 1.64736472 -0.1020457 -8.074672
## 2 2009-01-02 0.32981671 -0.2377234 7.617473
## 3 2009-01-03 0.05010405 -0.7091054 -9.770047
## 4 2009-01-04 0.41187479 1.1899260 -1.655071
## 5 2009-01-05 -2.20625659 -1.1299452 1.615068
# TRUE (this fully reverses what the gather function has done)
identical(tidyr::spread(stockGather, stock, price), stocks)
## [1] TRUE
# tidyr::separate()
df <- data.frame(x = c(NA, "a.b", "a.d", "b.c"))
df
## x
## 1 <NA>
## 2 a.b
## 3 a.d
## 4 b.c
# by default, the splits occur on anything that is not alphanumeric,
# so you get column A as whatever is before the dot and column B as whatever is after the dot
dfSep <- tidyr::separate(df, x, c("A", "B"))
dfSep
## A B
## 1 <NA> <NA>
## 2 a b
## 3 a d
## 4 b c
# tidyr::unite()
tidyr::unite(dfSep, united, c(A, B), sep="")
## united
## 1 NANA
## 2 ab
## 3 ad
## 4 bc
is.na(dfSep) # caution . . .
## A B
## 1 TRUE TRUE
## 2 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
is.na(tidyr::unite(dfSep, united, c(A, B), sep="")) # caution . . .
## united
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
The tolower() and toupper() commands can be very useful also
Example code includes:
# lubridate::ymd()
lubridate::ymd("160720")
## [1] "2016-07-20 UTC"
lubridate::ymd("2016-7-20")
## [1] "2016-07-20 UTC"
lubridate::ymd("16jul20")
## [1] "2016-07-20 UTC"
lubridate::ymd("16/07/20")
## [1] "2016-07-20 UTC"
# lubridate::hms()
lubridate::hms("07h15:00")
## [1] "7H 15M 0S"
lubridate::hms("17 hours, 15 minutes 00 seconds")
## [1] "17H 15M 0S"
lubridate::hms("07-15-00")
## [1] "7H 15M 0S"
# From ?stringr::str_detect
#
# str_detect(string, pattern)
# string Input vector. Either a character vector, or something coercible to one.
# pattern Pattern to look for. The default interpretation is a regular expression, as described in stringi-search-regex. Control options with regex(). Match a fixed string (i.e. by comparing only bytes), using fixed(x). This is fast, but approximate. Generally, for matching human text, you'll want coll(x) which respects character matching rules for the specified locale. Match character, word, line and sentence boundaries with boundary(). An empty pattern, "", is equivalent to boundary("character").
#
fruit <- c("apple", "banana", "pear", "pinapple")
stringr::str_detect(fruit, "a")
## [1] TRUE TRUE TRUE TRUE
stringr::str_detect(fruit, "^a")
## [1] TRUE FALSE FALSE FALSE
stringr::str_detect(fruit, "a$")
## [1] FALSE TRUE FALSE FALSE
stringr::str_detect(fruit, "b")
## [1] FALSE TRUE FALSE FALSE
stringr::str_detect(fruit, "[aeiou]")
## [1] TRUE TRUE TRUE TRUE
# Also vectorised over pattern
stringr::str_detect("aecfg", letters)
## [1] TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE
# From ?stringr::str_replace
#
# str_replace(string, pattern, replacement)
# str_replace_all(string, pattern, replacement)
# string Input vector. Either a character vector, or something coercible to one.
# pattern, replacement Supply separate pattern and replacement strings to vectorise over the patterns. References of the form \1, \2 will be replaced with the contents of the respective matched group (created by ()) within the pattern. For str_replace_all only, you can perform multiple patterns and replacements to each string, by passing a named character to pattern.
#
someNA <- c(letters, "", LETTERS, "")
someNA[someNA==""] <- NA
someNA
## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
## [18] "r" "s" "t" "u" "v" "w" "x" "y" "z" NA "A" "B" "C" "D" "E" "F" "G"
## [35] "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X"
## [52] "Y" "Z" NA
fruits <- c("one apple", "two pears", "three bananas")
stringr::str_replace(fruits, "[aeiou]", "-") # Replace FIRST instance
## [1] "-ne apple" "tw- pears" "thr-e bananas"
stringr::str_replace_all(fruits, "[aeiou]", "-") # Replace ALL instances
## [1] "-n- -ppl-" "tw- p--rs" "thr-- b-n-n-s"
stringr::str_replace(fruits, "([aeiou])", "\\1\\1\\1") # Triple up on the first vowel
## [1] "ooone apple" "twooo pears" "threeee bananas"
stringr::str_replace(fruits, "[aeiou]", c("1", "2", "3")) # First vowel to 1, 2, 3 in word 1, 2, 3
## [1] "1ne apple" "tw2 pears" "thr3e bananas"
stringr::str_replace(fruits, c("a", "e", "i"), "-") # First a -> - in word 1, first e -> - in word 2 . . .
## [1] "one -pple" "two p-ars" "three bananas"
stringr::str_replace_all(fruits, "([aeiou])", "\\1\\1") # Double up on all vowels
## [1] "oonee aapplee" "twoo peeaars" "threeee baanaanaas"
stringr::str_replace_all(fruits, "[aeiou]", c("1", "2", "3")) # All vowels to 1, 2, 3, in word 1, 2, 3
## [1] "1n1 1ppl1" "tw2 p22rs" "thr33 b3n3n3s"
stringr::str_replace_all(fruits, c("a", "e", "i"), "-") # All a -> - in word 1, . . .
## [1] "one -pple" "two p-ars" "three bananas"
Further, the outline from the weather gathering data cleaning challenge is noted:
The library(dplyr) is a grammar of data manipulation. It is written in C++ so you get the speed of C with the convenience of R. It is in essence the data frame to data frame portion of plyr (plyr was the original Split-Apply-Combine). May want to look in to count, transmute, and other verbs added post this summary.
The examples use data(hflights) from library(hflights):
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:purrr':
##
## order_by
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(hflights)
data(hflights)
head(hflights)
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## 5424 2011 1 1 6 1400 1500 AA
## 5425 2011 1 2 7 1401 1501 AA
## 5426 2011 1 3 1 1352 1502 AA
## 5427 2011 1 4 2 1403 1513 AA
## 5428 2011 1 5 3 1405 1507 AA
## 5429 2011 1 6 4 1359 1503 AA
## FlightNum TailNum ActualElapsedTime AirTime ArrDelay DepDelay Origin
## 5424 428 N576AA 60 40 -10 0 IAH
## 5425 428 N557AA 60 45 -9 1 IAH
## 5426 428 N541AA 70 48 -8 -8 IAH
## 5427 428 N403AA 70 39 3 3 IAH
## 5428 428 N492AA 62 44 -3 5 IAH
## 5429 428 N262AA 64 45 -7 -1 IAH
## Dest Distance TaxiIn TaxiOut Cancelled CancellationCode Diverted
## 5424 DFW 224 7 13 0 0
## 5425 DFW 224 6 9 0 0
## 5426 DFW 224 5 17 0 0
## 5427 DFW 224 9 22 0 0
## 5428 DFW 224 9 9 0 0
## 5429 DFW 224 6 13 0 0
summary(hflights)
## Year Month DayofMonth DayOfWeek
## Min. :2011 Min. : 1.000 Min. : 1.00 Min. :1.000
## 1st Qu.:2011 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.:2.000
## Median :2011 Median : 7.000 Median :16.00 Median :4.000
## Mean :2011 Mean : 6.514 Mean :15.74 Mean :3.948
## 3rd Qu.:2011 3rd Qu.: 9.000 3rd Qu.:23.00 3rd Qu.:6.000
## Max. :2011 Max. :12.000 Max. :31.00 Max. :7.000
##
## DepTime ArrTime UniqueCarrier FlightNum
## Min. : 1 Min. : 1 Length:227496 Min. : 1
## 1st Qu.:1021 1st Qu.:1215 Class :character 1st Qu.: 855
## Median :1416 Median :1617 Mode :character Median :1696
## Mean :1396 Mean :1578 Mean :1962
## 3rd Qu.:1801 3rd Qu.:1953 3rd Qu.:2755
## Max. :2400 Max. :2400 Max. :7290
## NA's :2905 NA's :3066
## TailNum ActualElapsedTime AirTime ArrDelay
## Length:227496 Min. : 34.0 Min. : 11.0 Min. :-70.000
## Class :character 1st Qu.: 77.0 1st Qu.: 58.0 1st Qu.: -8.000
## Mode :character Median :128.0 Median :107.0 Median : 0.000
## Mean :129.3 Mean :108.1 Mean : 7.094
## 3rd Qu.:165.0 3rd Qu.:141.0 3rd Qu.: 11.000
## Max. :575.0 Max. :549.0 Max. :978.000
## NA's :3622 NA's :3622 NA's :3622
## DepDelay Origin Dest Distance
## Min. :-33.000 Length:227496 Length:227496 Min. : 79.0
## 1st Qu.: -3.000 Class :character Class :character 1st Qu.: 376.0
## Median : 0.000 Mode :character Mode :character Median : 809.0
## Mean : 9.445 Mean : 787.8
## 3rd Qu.: 9.000 3rd Qu.:1042.0
## Max. :981.000 Max. :3904.0
## NA's :2905
## TaxiIn TaxiOut Cancelled CancellationCode
## Min. : 1.000 Min. : 1.00 Min. :0.00000 Length:227496
## 1st Qu.: 4.000 1st Qu.: 10.00 1st Qu.:0.00000 Class :character
## Median : 5.000 Median : 14.00 Median :0.00000 Mode :character
## Mean : 6.099 Mean : 15.09 Mean :0.01307
## 3rd Qu.: 7.000 3rd Qu.: 18.00 3rd Qu.:0.00000
## Max. :165.000 Max. :163.00 Max. :1.00000
## NA's :3066 NA's :2947
## Diverted
## Min. :0.000000
## 1st Qu.:0.000000
## Median :0.000000
## Mean :0.002853
## 3rd Qu.:0.000000
## Max. :1.000000
##
The “tbl” is a special type of data frame, which is very helpful for printing:
An interesting way to do a lookup table:
See for example:
lut <- c("AA" = "American", "AS" = "Alaska", "B6" = "JetBlue", "CO" = "Continental",
"DL" = "Delta", "OO" = "SkyWest", "UA" = "United", "US" = "US_Airways",
"WN" = "Southwest", "EV" = "Atlantic_Southeast", "F9" = "Frontier",
"FL" = "AirTran", "MQ" = "American_Eagle", "XE" = "ExpressJet", "YV" = "Mesa"
)
hflights$Carrier <- lut[hflights$UniqueCarrier]
glimpse(hflights)
## Observations: 227,496
## Variables: 22
## $ Year (int) 2011, 2011, 2011, 2011, 2011, 2011, 2011, 20...
## $ Month (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ DayofMonth (int) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1...
## $ DayOfWeek (int) 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6,...
## $ DepTime (int) 1400, 1401, 1352, 1403, 1405, 1359, 1359, 13...
## $ ArrTime (int) 1500, 1501, 1502, 1513, 1507, 1503, 1509, 14...
## $ UniqueCarrier (chr) "AA", "AA", "AA", "AA", "AA", "AA", "AA", "A...
## $ FlightNum (int) 428, 428, 428, 428, 428, 428, 428, 428, 428,...
## $ TailNum (chr) "N576AA", "N557AA", "N541AA", "N403AA", "N49...
## $ ActualElapsedTime (int) 60, 60, 70, 70, 62, 64, 70, 59, 71, 70, 70, ...
## $ AirTime (int) 40, 45, 48, 39, 44, 45, 43, 40, 41, 45, 42, ...
## $ ArrDelay (int) -10, -9, -8, 3, -3, -7, -1, -16, 44, 43, 29,...
## $ DepDelay (int) 0, 1, -8, 3, 5, -1, -1, -5, 43, 43, 29, 19, ...
## $ Origin (chr) "IAH", "IAH", "IAH", "IAH", "IAH", "IAH", "I...
## $ Dest (chr) "DFW", "DFW", "DFW", "DFW", "DFW", "DFW", "D...
## $ Distance (int) 224, 224, 224, 224, 224, 224, 224, 224, 224,...
## $ TaxiIn (int) 7, 6, 5, 9, 9, 6, 12, 7, 8, 6, 8, 4, 6, 5, 6...
## $ TaxiOut (int) 13, 9, 17, 22, 9, 13, 15, 12, 22, 19, 20, 11...
## $ Cancelled (int) 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ CancellationCode (chr) "", "", "", "", "", "", "", "", "", "", "", ...
## $ Diverted (int) 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ Carrier (chr) "American", "American", "American", "America...
There are five main verbs in dplyr:
There is also the group_by capability for summaries of sub-groups:
The dplyr library can also work with databases. It only loads the data that you need, and you do not need to know the relevant SQL code – dplyr writes the SQL code for you.
Basic select and mutate examples include:
data(hflights)
# Make it faster, as well as a prettier printer
hflights <- tbl_df(hflights)
hflights
## Source: local data frame [227,496 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 1 6 1400 1500 AA
## 2 2011 1 2 7 1401 1501 AA
## 3 2011 1 3 1 1352 1502 AA
## 4 2011 1 4 2 1403 1513 AA
## 5 2011 1 5 3 1405 1507 AA
## 6 2011 1 6 4 1359 1503 AA
## 7 2011 1 7 5 1359 1509 AA
## 8 2011 1 8 6 1355 1454 AA
## 9 2011 1 9 7 1443 1554 AA
## 10 2011 1 10 1 1443 1553 AA
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
class(hflights)
## [1] "tbl_df" "tbl" "data.frame"
# Select examples
select(hflights, ActualElapsedTime, AirTime, ArrDelay, DepDelay)
## Source: local data frame [227,496 x 4]
##
## ActualElapsedTime AirTime ArrDelay DepDelay
## (int) (int) (int) (int)
## 1 60 40 -10 0
## 2 60 45 -9 1
## 3 70 48 -8 -8
## 4 70 39 3 3
## 5 62 44 -3 5
## 6 64 45 -7 -1
## 7 70 43 -1 -1
## 8 59 40 -16 -5
## 9 71 41 44 43
## 10 70 45 43 43
## .. ... ... ... ...
select(hflights, Origin:Cancelled)
## Source: local data frame [227,496 x 6]
##
## Origin Dest Distance TaxiIn TaxiOut Cancelled
## (chr) (chr) (int) (int) (int) (int)
## 1 IAH DFW 224 7 13 0
## 2 IAH DFW 224 6 9 0
## 3 IAH DFW 224 5 17 0
## 4 IAH DFW 224 9 22 0
## 5 IAH DFW 224 9 9 0
## 6 IAH DFW 224 6 13 0
## 7 IAH DFW 224 12 15 0
## 8 IAH DFW 224 7 12 0
## 9 IAH DFW 224 8 22 0
## 10 IAH DFW 224 6 19 0
## .. ... ... ... ... ... ...
select(hflights, Year:DayOfWeek, ArrDelay:Diverted)
## Source: local data frame [227,496 x 14]
##
## Year Month DayofMonth DayOfWeek ArrDelay DepDelay Origin Dest
## (int) (int) (int) (int) (int) (int) (chr) (chr)
## 1 2011 1 1 6 -10 0 IAH DFW
## 2 2011 1 2 7 -9 1 IAH DFW
## 3 2011 1 3 1 -8 -8 IAH DFW
## 4 2011 1 4 2 3 3 IAH DFW
## 5 2011 1 5 3 -3 5 IAH DFW
## 6 2011 1 6 4 -7 -1 IAH DFW
## 7 2011 1 7 5 -1 -1 IAH DFW
## 8 2011 1 8 6 -16 -5 IAH DFW
## 9 2011 1 9 7 44 43 IAH DFW
## 10 2011 1 10 1 43 43 IAH DFW
## .. ... ... ... ... ... ... ... ...
## Variables not shown: Distance (int), TaxiIn (int), TaxiOut (int),
## Cancelled (int), CancellationCode (chr), Diverted (int)
select(hflights, ends_with("Delay"))
## Source: local data frame [227,496 x 2]
##
## ArrDelay DepDelay
## (int) (int)
## 1 -10 0
## 2 -9 1
## 3 -8 -8
## 4 3 3
## 5 -3 5
## 6 -7 -1
## 7 -1 -1
## 8 -16 -5
## 9 44 43
## 10 43 43
## .. ... ...
select(hflights, UniqueCarrier, ends_with("Num"), starts_with("Cancel"))
## Source: local data frame [227,496 x 5]
##
## UniqueCarrier FlightNum TailNum Cancelled CancellationCode
## (chr) (int) (chr) (int) (chr)
## 1 AA 428 N576AA 0
## 2 AA 428 N557AA 0
## 3 AA 428 N541AA 0
## 4 AA 428 N403AA 0
## 5 AA 428 N492AA 0
## 6 AA 428 N262AA 0
## 7 AA 428 N493AA 0
## 8 AA 428 N477AA 0
## 9 AA 428 N476AA 0
## 10 AA 428 N504AA 0
## .. ... ... ... ... ...
select(hflights, ends_with("Time"), ends_with("Delay"))
## Source: local data frame [227,496 x 6]
##
## DepTime ArrTime ActualElapsedTime AirTime ArrDelay DepDelay
## (int) (int) (int) (int) (int) (int)
## 1 1400 1500 60 40 -10 0
## 2 1401 1501 60 45 -9 1
## 3 1352 1502 70 48 -8 -8
## 4 1403 1513 70 39 3 3
## 5 1405 1507 62 44 -3 5
## 6 1359 1503 64 45 -7 -1
## 7 1359 1509 70 43 -1 -1
## 8 1355 1454 59 40 -16 -5
## 9 1443 1554 71 41 44 43
## 10 1443 1553 70 45 43 43
## .. ... ... ... ... ... ...
# Mutate example
m1 <- mutate(hflights, loss = ArrDelay - DepDelay, loss_ratio = loss / DepDelay)
class(m1)
## [1] "tbl_df" "tbl" "data.frame"
m1
## Source: local data frame [227,496 x 23]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 1 6 1400 1500 AA
## 2 2011 1 2 7 1401 1501 AA
## 3 2011 1 3 1 1352 1502 AA
## 4 2011 1 4 2 1403 1513 AA
## 5 2011 1 5 3 1405 1507 AA
## 6 2011 1 6 4 1359 1503 AA
## 7 2011 1 7 5 1359 1509 AA
## 8 2011 1 8 6 1355 1454 AA
## 9 2011 1 9 7 1443 1554 AA
## 10 2011 1 10 1 1443 1553 AA
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int), loss (int), loss_ratio (dbl)
glimpse(m1)
## Observations: 227,496
## Variables: 23
## $ Year (int) 2011, 2011, 2011, 2011, 2011, 2011, 2011, 20...
## $ Month (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ DayofMonth (int) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1...
## $ DayOfWeek (int) 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6,...
## $ DepTime (int) 1400, 1401, 1352, 1403, 1405, 1359, 1359, 13...
## $ ArrTime (int) 1500, 1501, 1502, 1513, 1507, 1503, 1509, 14...
## $ UniqueCarrier (chr) "AA", "AA", "AA", "AA", "AA", "AA", "AA", "A...
## $ FlightNum (int) 428, 428, 428, 428, 428, 428, 428, 428, 428,...
## $ TailNum (chr) "N576AA", "N557AA", "N541AA", "N403AA", "N49...
## $ ActualElapsedTime (int) 60, 60, 70, 70, 62, 64, 70, 59, 71, 70, 70, ...
## $ AirTime (int) 40, 45, 48, 39, 44, 45, 43, 40, 41, 45, 42, ...
## $ ArrDelay (int) -10, -9, -8, 3, -3, -7, -1, -16, 44, 43, 29,...
## $ DepDelay (int) 0, 1, -8, 3, 5, -1, -1, -5, 43, 43, 29, 19, ...
## $ Origin (chr) "IAH", "IAH", "IAH", "IAH", "IAH", "IAH", "I...
## $ Dest (chr) "DFW", "DFW", "DFW", "DFW", "DFW", "DFW", "D...
## $ Distance (int) 224, 224, 224, 224, 224, 224, 224, 224, 224,...
## $ TaxiIn (int) 7, 6, 5, 9, 9, 6, 12, 7, 8, 6, 8, 4, 6, 5, 6...
## $ TaxiOut (int) 13, 9, 17, 22, 9, 13, 15, 12, 22, 19, 20, 11...
## $ Cancelled (int) 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ CancellationCode (chr) "", "", "", "", "", "", "", "", "", "", "", ...
## $ Diverted (int) 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ loss (int) -10, -10, 0, 0, -8, -6, 0, -11, 1, 0, 0, -14...
## $ loss_ratio (dbl) -Inf, -10.00000000, 0.00000000, 0.00000000, ...
Additionally, examples for filter and arrange:
# Examples for filter
filter(hflights, Distance >= 3000) # All flights that traveled 3000 miles or more
## Source: local data frame [527 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 31 1 924 1413 CO
## 2 2011 1 30 7 925 1410 CO
## 3 2011 1 29 6 1045 1445 CO
## 4 2011 1 28 5 1516 1916 CO
## 5 2011 1 27 4 950 1344 CO
## 6 2011 1 26 3 944 1350 CO
## 7 2011 1 25 2 924 1337 CO
## 8 2011 1 24 1 1144 1605 CO
## 9 2011 1 23 7 926 1335 CO
## 10 2011 1 22 6 942 1340 CO
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
filter(hflights, UniqueCarrier %in% c("B6", "WN", "DL"))
## Source: local data frame [48,679 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 1 6 654 1124 B6
## 2 2011 1 1 6 1639 2110 B6
## 3 2011 1 2 7 703 1113 B6
## 4 2011 1 2 7 1604 2040 B6
## 5 2011 1 3 1 659 1100 B6
## 6 2011 1 3 1 1801 2200 B6
## 7 2011 1 4 2 654 1103 B6
## 8 2011 1 4 2 1608 2034 B6
## 9 2011 1 5 3 700 1103 B6
## 10 2011 1 5 3 1544 1954 B6
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
filter(hflights, (TaxiIn + TaxiOut) > AirTime) # Flights where taxiing took longer than flying
## Source: local data frame [1,389 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 24 1 731 904 AA
## 2 2011 1 30 7 1959 2132 AA
## 3 2011 1 24 1 1621 1749 AA
## 4 2011 1 10 1 941 1113 AA
## 5 2011 1 31 1 1301 1356 CO
## 6 2011 1 31 1 2113 2215 CO
## 7 2011 1 31 1 1434 1539 CO
## 8 2011 1 31 1 900 1006 CO
## 9 2011 1 30 7 1304 1408 CO
## 10 2011 1 30 7 2004 2128 CO
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
filter(hflights, DepTime < 500 | ArrTime > 2200) # Flights departed before 5am or arrived after 10pm
## Source: local data frame [27,799 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 4 2 2100 2207 AA
## 2 2011 1 14 5 2119 2229 AA
## 3 2011 1 10 1 1934 2235 AA
## 4 2011 1 26 3 1905 2211 AA
## 5 2011 1 30 7 1856 2209 AA
## 6 2011 1 9 7 1938 2228 AS
## 7 2011 1 31 1 1919 2231 CO
## 8 2011 1 31 1 2116 2344 CO
## 9 2011 1 31 1 1850 2211 CO
## 10 2011 1 31 1 2102 2216 CO
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
filter(hflights, DepDelay > 0, ArrDelay < 0) # Flights that departed late but arrived ahead of schedule
## Source: local data frame [27,712 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 2 7 1401 1501 AA
## 2 2011 1 5 3 1405 1507 AA
## 3 2011 1 18 2 1408 1508 AA
## 4 2011 1 18 2 721 827 AA
## 5 2011 1 12 3 2015 2113 AA
## 6 2011 1 13 4 2020 2116 AA
## 7 2011 1 26 3 2009 2103 AA
## 8 2011 1 1 6 1631 1736 AA
## 9 2011 1 10 1 1639 1740 AA
## 10 2011 1 12 3 1631 1739 AA
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
filter(hflights, Cancelled == 1, DepDelay > 0) # Flights that were cancelled after being delayed
## Source: local data frame [40 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 26 3 1926 NA CO
## 2 2011 1 11 2 1100 NA US
## 3 2011 1 19 3 1811 NA XE
## 4 2011 1 7 5 2028 NA XE
## 5 2011 2 4 5 1638 NA AA
## 6 2011 2 8 2 1057 NA CO
## 7 2011 2 2 3 802 NA XE
## 8 2011 2 9 3 904 NA XE
## 9 2011 2 1 2 1508 NA OO
## 10 2011 3 31 4 1016 NA CO
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
c1 <- filter(hflights, Dest == "JFK") # Flights that had JFK as their destination: c1
c2 <- mutate(c1, Date = paste(Year, Month, DayofMonth, sep="-")) # Create a Date column: c2
select(c2, Date, DepTime, ArrTime, TailNum) # Print out a selection of columns of c2
## Source: local data frame [695 x 4]
##
## Date DepTime ArrTime TailNum
## (chr) (int) (int) (chr)
## 1 2011-1-1 654 1124 N324JB
## 2 2011-1-1 1639 2110 N324JB
## 3 2011-1-2 703 1113 N324JB
## 4 2011-1-2 1604 2040 N324JB
## 5 2011-1-3 659 1100 N229JB
## 6 2011-1-3 1801 2200 N206JB
## 7 2011-1-4 654 1103 N267JB
## 8 2011-1-4 1608 2034 N267JB
## 9 2011-1-5 700 1103 N708JB
## 10 2011-1-5 1544 1954 N644JB
## .. ... ... ... ...
dtc <- filter(hflights, Cancelled == 1, !is.na(DepDelay)) # Definition of dtc
# Examples for arrange
arrange(dtc, DepDelay) # Arrange dtc by departure delays
## Source: local data frame [68 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 7 23 6 605 NA F9
## 2 2011 1 17 1 916 NA XE
## 3 2011 12 1 4 541 NA US
## 4 2011 10 12 3 2022 NA MQ
## 5 2011 7 29 5 1424 NA CO
## 6 2011 9 29 4 1639 NA OO
## 7 2011 2 9 3 555 NA MQ
## 8 2011 5 9 1 715 NA OO
## 9 2011 1 20 4 1413 NA UA
## 10 2011 1 17 1 831 NA WN
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
arrange(dtc, CancellationCode) # Arrange dtc so that cancellation reasons are grouped
## Source: local data frame [68 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 1 20 4 1413 NA UA
## 2 2011 1 7 5 2028 NA XE
## 3 2011 2 4 5 1638 NA AA
## 4 2011 2 8 2 1057 NA CO
## 5 2011 2 1 2 1508 NA OO
## 6 2011 2 21 1 2257 NA OO
## 7 2011 2 9 3 555 NA MQ
## 8 2011 3 18 5 727 NA UA
## 9 2011 4 4 1 1632 NA DL
## 10 2011 4 8 5 1608 NA WN
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
arrange(dtc, UniqueCarrier, DepDelay) # Arrange dtc according to carrier and departure delays
## Source: local data frame [68 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 8 18 4 1808 NA AA
## 2 2011 2 4 5 1638 NA AA
## 3 2011 7 29 5 1424 NA CO
## 4 2011 1 26 3 1703 NA CO
## 5 2011 8 11 4 1320 NA CO
## 6 2011 7 25 1 1654 NA CO
## 7 2011 1 26 3 1926 NA CO
## 8 2011 3 31 4 1016 NA CO
## 9 2011 2 8 2 1057 NA CO
## 10 2011 4 4 1 1632 NA DL
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
arrange(hflights, UniqueCarrier, desc(DepDelay)) # Arrange by carrier and decreasing departure delays
## Source: local data frame [227,496 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 12 12 1 650 808 AA
## 2 2011 11 19 6 1752 1910 AA
## 3 2011 12 22 4 1728 1848 AA
## 4 2011 10 23 7 2305 2 AA
## 5 2011 9 27 2 1206 1300 AA
## 6 2011 3 17 4 1647 1747 AA
## 7 2011 6 21 2 955 1315 AA
## 8 2011 5 20 5 2359 130 AA
## 9 2011 4 19 2 2023 2142 AA
## 10 2011 5 12 4 2133 53 AA
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
arrange(hflights, DepDelay + ArrDelay) # Arrange flights by total delay (normal order)
## Source: local data frame [227,496 x 21]
##
## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
## (int) (int) (int) (int) (int) (int) (chr)
## 1 2011 7 3 7 1914 2039 XE
## 2 2011 8 31 3 934 1039 OO
## 3 2011 8 21 7 935 1039 OO
## 4 2011 8 28 7 2059 2206 OO
## 5 2011 8 29 1 935 1041 OO
## 6 2011 12 25 7 741 926 OO
## 7 2011 1 30 7 620 812 OO
## 8 2011 8 3 3 1741 1810 XE
## 9 2011 8 4 4 930 1041 OO
## 10 2011 8 18 4 939 1043 OO
## .. ... ... ... ... ... ... ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
## (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
## (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
## CancellationCode (chr), Diverted (int)
Additionally, examples for the summarize verb:
# Print out a summary with variables min_dist and max_dist
summarize(hflights, min_dist = min(Distance), max_dist = max(Distance))
## Source: local data frame [1 x 2]
##
## min_dist max_dist
## (int) (int)
## 1 79 3904
# Print out a summary with variable max_div
summarize(filter(hflights, Diverted == 1), max_div = max(Distance))
## Source: local data frame [1 x 1]
##
## max_div
## (int)
## 1 3904
# Remove rows that have NA ArrDelay: temp1
temp1 <- filter(hflights, !is.na(ArrDelay))
# Generate summary about ArrDelay column of temp1
summarize(temp1, earliest=min(ArrDelay), average=mean(ArrDelay), latest=max(ArrDelay), sd=sd(ArrDelay))
## Source: local data frame [1 x 4]
##
## earliest average latest sd
## (int) (dbl) (int) (dbl)
## 1 -70 7.094334 978 30.70852
# Keep rows that have no NA TaxiIn and no NA TaxiOut: temp2
temp2 <- filter(hflights, !is.na(TaxiIn), !is.na(TaxiOut))
# Print the maximum taxiing difference of temp2 with summarise()
summarize(temp2, max_taxi_diff = max(abs(TaxiIn - TaxiOut)))
## Source: local data frame [1 x 1]
##
## max_taxi_diff
## (int)
## 1 160
# Generate summarizing statistics for hflights
summarize(hflights, n_obs = n(), n_carrier = n_distinct(UniqueCarrier), n_dest = n_distinct(Dest))
## Source: local data frame [1 x 3]
##
## n_obs n_carrier n_dest
## (int) (int) (int)
## 1 227496 15 116
# All American Airline flights
aa <- filter(hflights, UniqueCarrier == "AA")
# Generate summarizing statistics for aa
summarize(aa, n_flights = n(), n_canc = sum(Cancelled), avg_delay = mean(ArrDelay, na.rm=TRUE))
## Source: local data frame [1 x 3]
##
## n_flights n_canc avg_delay
## (int) (int) (dbl)
## 1 3244 60 0.8917558
Additionally, examples for the pipe/chain as per magrittr:
# Find the average delta in taxi times
hflights %>%
mutate(diff = (TaxiOut - TaxiIn)) %>%
filter(!is.na(diff)) %>%
summarize(avg = mean(diff))
## Source: local data frame [1 x 1]
##
## avg
## (dbl)
## 1 8.992064
# Find flights that average less than 70 mph assuming 100 wasted minutes per flight
hflights %>%
mutate(RealTime = ActualElapsedTime + 100, mph = 60 * Distance / RealTime) %>%
filter(!is.na(mph), mph < 70) %>%
summarize(n_less = n(), n_dest = n_distinct(Dest), min_dist = min(Distance), max_dist = max(Distance))
## Source: local data frame [1 x 4]
##
## n_less n_dest min_dist max_dist
## (int) (int) (int) (int)
## 1 6726 13 79 305
# Find flights that average less than 105 mph, or that are diverted/cancelled
hflights %>%
mutate(RealTime = ActualElapsedTime + 100, mph = Distance / RealTime * 60) %>%
filter(mph < 105 | Cancelled == 1 | Diverted == 1) %>%
summarize(n_non = n(), n_dest = n_distinct(Dest), min_dist = min(Distance), max_dist = max(Distance))
## Source: local data frame [1 x 4]
##
## n_non n_dest min_dist max_dist
## (int) (int) (int) (int)
## 1 42400 113 79 3904
# Find overnight flights
filter(hflights, !is.na(DepTime), !is.na(ArrTime), DepTime > ArrTime) %>%
summarize(num = n())
## Source: local data frame [1 x 1]
##
## num
## (int)
## 1 2718
There is also the group_by capability, typically for use with summarize:
# Make an ordered per-carrier summary of hflights
group_by(hflights, UniqueCarrier) %>%
summarize(p_canc = 100 * mean(Cancelled, na.rm=TRUE), avg_delay = mean(ArrDelay, na.rm=TRUE)) %>%
arrange(avg_delay, p_canc)
## Source: local data frame [15 x 3]
##
## UniqueCarrier p_canc avg_delay
## (chr) (dbl) (dbl)
## 1 US 1.1268986 -0.6307692
## 2 AA 1.8495684 0.8917558
## 3 FL 0.9817672 1.8536239
## 4 AS 0.0000000 3.1923077
## 5 YV 1.2658228 4.0128205
## 6 DL 1.5903067 6.0841374
## 7 CO 0.6782614 6.0986983
## 8 MQ 2.9044750 7.1529751
## 9 EV 3.4482759 7.2569543
## 10 WN 1.5504047 7.5871430
## 11 F9 0.7159905 7.6682692
## 12 XE 1.5495599 8.1865242
## 13 OO 1.3946828 8.6934922
## 14 B6 2.5899281 9.8588410
## 15 UA 1.6409266 10.4628628
# Ordered overview of average arrival delays per carrier
hflights %>%
filter(!is.na(ArrDelay), ArrDelay > 0) %>%
group_by(UniqueCarrier) %>%
summarize(avg = mean(ArrDelay)) %>%
mutate(rank = rank(avg)) %>%
arrange(rank)
## Source: local data frame [15 x 3]
##
## UniqueCarrier avg rank
## (chr) (dbl) (dbl)
## 1 YV 18.67568 1
## 2 F9 18.68683 2
## 3 US 20.70235 3
## 4 CO 22.13374 4
## 5 AS 22.91195 5
## 6 OO 24.14663 6
## 7 XE 24.19337 7
## 8 WN 25.27750 8
## 9 FL 27.85693 9
## 10 AA 28.49740 10
## 11 DL 32.12463 11
## 12 UA 32.48067 12
## 13 MQ 38.75135 13
## 14 EV 40.24231 14
## 15 B6 45.47744 15
# How many airplanes only flew to one destination?
hflights %>%
group_by(TailNum) %>%
summarise(destPerTail = n_distinct(Dest)) %>%
filter(destPerTail == 1) %>%
summarise(nplanes=n())
## Source: local data frame [1 x 1]
##
## nplanes
## (int)
## 1 1526
# Find the most visited destination for each carrier
hflights %>%
group_by(UniqueCarrier, Dest) %>%
summarise(n = n()) %>%
mutate(rank = rank(-n)) %>%
filter(rank == 1)
## Source: local data frame [15 x 4]
## Groups: UniqueCarrier [15]
##
## UniqueCarrier Dest n rank
## (chr) (chr) (int) (dbl)
## 1 AA DFW 2105 1
## 2 AS SEA 365 1
## 3 B6 JFK 695 1
## 4 CO EWR 3924 1
## 5 DL ATL 2396 1
## 6 EV DTW 851 1
## 7 F9 DEN 837 1
## 8 FL ATL 2029 1
## 9 MQ DFW 2424 1
## 10 OO COS 1335 1
## 11 UA SFO 643 1
## 12 US CLT 2212 1
## 13 WN DAL 8243 1
## 14 XE CRP 3175 1
## 15 YV CLT 71 1
# Use summarise to calculate n_carrier
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, last
## The following object is masked from 'package:purrr':
##
## transpose
hflights2 <- as.data.table(hflights)
hflights2 %>%
summarize(n_carrier = n_distinct(UniqueCarrier))
## n_carrier
## 1: 15
And, dplyr can be used with databases, including writing the SQL query that matches to the dplyr request. The results are cached to avoid constantly pinging the server:
# Set up a connection to the mysql database
my_db <- src_mysql(dbname = "dplyr",
host = "courses.csrrinzqubik.us-east-1.rds.amazonaws.com",
port = 3306,
user = "student",
password = "datacamp")
# Reference a table within that source: nycflights
nycflights <- tbl(my_db, "dplyr")
# glimpse at nycflights
glimpse(nycflights)
## Observations: 336,776
## Variables: 17
## $ id (int) 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1...
## $ year (int) 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013...
## $ month (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
## $ day (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
## $ dep_time (int) 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 55...
## $ dep_delay (int) 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2,...
## $ arr_time (int) 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 8...
## $ arr_delay (int) 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7,...
## $ carrier (chr) "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6"...
## $ tailnum (chr) "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N...
## $ flight (int) 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301...
## $ origin (chr) "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LG...
## $ dest (chr) "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IA...
## $ air_time (int) 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149...
## $ distance (int) 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 73...
## $ hour (int) 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6...
## $ minute (int) 17, 33, 42, 44, 54, 54, 55, 57, 57, 58, 58, 58, 58, ...
# Ordered, grouped summary of nycflights
nycflights %>%
group_by(carrier) %>%
summarize(n_flights = n(), avg_delay = mean(arr_delay)) %>%
arrange(avg_delay)
## Source: mysql 5.6.23-log [student@courses.csrrinzqubik.us-east-1.rds.amazonaws.com:/dplyr]
## From: <derived table> [?? x 3]
## Arrange: avg_delay
## Warning in .local(conn, statement, ...): Decimal MySQL column 2 imported as
## numeric
## carrier n_flights avg_delay
## (chr) (dbl) (dbl)
## 1 AS 714 -9.8613
## 2 HA 342 -6.9152
## 3 AA 32729 0.3556
## 4 DL 48110 1.6289
## 5 VX 5162 1.7487
## 6 US 20536 2.0565
## 7 UA 58665 3.5045
## 8 9E 18460 6.9135
## 9 B6 54635 9.3565
## 10 WN 12275 9.4675
## .. ... ... ...
The data.table library is designed to simplify and speed up work with large datasets. The language is broadly analogous to SQL, with syntax that includes equivalents for SELECT, WHERE, and GROUP BY. Some general attributes of a data.table object include:
NOTE - all data.table are also data.frame, and if a package is not aware of data.table, then it will act as data.frame for that package.
General syntax is:
Example table creation:
Some example code includes:
library(data.table)
DT <- data.table(a = c(1, 2), b=LETTERS[1:4])
str(DT)
## Classes 'data.table' and 'data.frame': 4 obs. of 2 variables:
## $ a: num 1 2 1 2
## $ b: chr "A" "B" "C" "D"
## - attr(*, ".internal.selfref")=<externalptr>
DT
## a b
## 1: 1 A
## 2: 2 B
## 3: 1 C
## 4: 2 D
# Print the second to last row of DT using .N
DT[.N-1]
## a b
## 1: 1 C
# Print the column names of DT
names(DT)
## [1] "a" "b"
# Print the number or rows and columns of DT
dim(DT)
## [1] 4 2
# Select row 2 twice and row 3, returning a data.table with three rows where row 2 is a duplicate of row 1.
DT[c(2, 2:3)]
## a b
## 1: 2 B
## 2: 2 B
## 3: 1 C
DT <- data.table(A = 1:5, B = letters[1:5], C = 6:10)
str(DT)
## Classes 'data.table' and 'data.frame': 5 obs. of 3 variables:
## $ A: int 1 2 3 4 5
## $ B: chr "a" "b" "c" "d" ...
## $ C: int 6 7 8 9 10
## - attr(*, ".internal.selfref")=<externalptr>
DT
## A B C
## 1: 1 a 6
## 2: 2 b 7
## 3: 3 c 8
## 4: 4 d 9
## 5: 5 e 10
# Subset rows 1 and 3, and columns B and C
DT[c(1, 3), .(B, C)]
## B C
## 1: a 6
## 2: c 8
# Assign to ans the correct value
ans <- DT[ , .(B, val=A*C)]
ans
## B val
## 1: a 6
## 2: b 14
## 3: c 24
## 4: d 36
## 5: e 50
# Fill in the blanks such that ans2 equals target
target <- data.table(B = c("a", "b", "c", "d", "e", "a", "b", "c", "d", "e"),
val = as.integer(c(6:10, 1:5))
)
ans2 <- DT[, .(B, val = c(C, A))]
identical(target, ans2)
## [1] TRUE
DT <- as.data.table(iris)
str(DT)
## Classes 'data.table' and 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, ".internal.selfref")=<externalptr>
# For each Species, print the mean Sepal.Length
DT[ , mean(Sepal.Length), Species]
## Species V1
## 1: setosa 5.006
## 2: versicolor 5.936
## 3: virginica 6.588
# Print mean Sepal.Length, grouping by first letter of Species
DT[ , mean(Sepal.Length), substr(Species, 1, 1)]
## substr V1
## 1: s 5.006
## 2: v 6.262
str(DT)
## Classes 'data.table' and 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, ".internal.selfref")=<externalptr>
identical(DT, as.data.table(iris))
## [1] TRUE
# Group the specimens by Sepal area (to the nearest 10 cm2) and count how many occur in each group.
DT[, .N, by = 10 * round(Sepal.Length * Sepal.Width / 10)]
## round N
## 1: 20 117
## 2: 10 29
## 3: 30 4
# Now name the output columns `Area` and `Count`
DT[, .(Count=.N), by = .(Area = 10 * round(Sepal.Length * Sepal.Width / 10))]
## Area Count
## 1: 20 117
## 2: 10 29
## 3: 30 4
# Create the data.table DT
set.seed(1L)
DT <- data.table(A = rep(letters[2:1], each = 4L),
B = rep(1:4, each = 2L),
C = sample(8)
)
str(DT)
## Classes 'data.table' and 'data.frame': 8 obs. of 3 variables:
## $ A: chr "b" "b" "b" "b" ...
## $ B: int 1 1 2 2 3 3 4 4
## $ C: int 3 8 4 5 1 7 2 6
## - attr(*, ".internal.selfref")=<externalptr>
DT
## A B C
## 1: b 1 3
## 2: b 1 8
## 3: b 2 4
## 4: b 2 5
## 5: a 3 1
## 6: a 3 7
## 7: a 4 2
## 8: a 4 6
# Create the new data.table, DT2
DT2 <- DT[, .(C = cumsum(C)), by = .(A, B)]
str(DT2)
## Classes 'data.table' and 'data.frame': 8 obs. of 3 variables:
## $ A: chr "b" "b" "b" "b" ...
## $ B: int 1 1 2 2 3 3 4 4
## $ C: int 3 11 4 9 1 8 2 8
## - attr(*, ".internal.selfref")=<externalptr>
DT2
## A B C
## 1: b 1 3
## 2: b 1 11
## 3: b 2 4
## 4: b 2 9
## 5: a 3 1
## 6: a 3 8
## 7: a 4 2
## 8: a 4 8
# Select from DT2 the last two values from C while you group by A
DT2[, .(C = tail(C, 2)), by = A]
## A C
## 1: b 4
## 2: b 9
## 3: a 2
## 4: a 8
The chaining operation in data.table is run as [statement][next statement].
Example code includes:
set.seed(1L)
DT <- data.table(A = rep(letters[2:1], each = 4L),
B = rep(1:4, each = 2L),
C = sample(8))
str(DT)
## Classes 'data.table' and 'data.frame': 8 obs. of 3 variables:
## $ A: chr "b" "b" "b" "b" ...
## $ B: int 1 1 2 2 3 3 4 4
## $ C: int 3 8 4 5 1 7 2 6
## - attr(*, ".internal.selfref")=<externalptr>
DT
## A B C
## 1: b 1 3
## 2: b 1 8
## 3: b 2 4
## 4: b 2 5
## 5: a 3 1
## 6: a 3 7
## 7: a 4 2
## 8: a 4 6
# Perform operation using chaining
DT[ , .(C = cumsum(C)), by = .(A, B)][ , .(C = tail(C, 2)), by=.(A)]
## A C
## 1: b 4
## 2: b 9
## 3: a 2
## 4: a 8
data(iris)
DT <- as.data.table(iris)
str(DT)
## Classes 'data.table' and 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, ".internal.selfref")=<externalptr>
# Perform chained operations on DT
DT[ , .(Sepal.Length = median(Sepal.Length), Sepal.Width = median(Sepal.Width),
Petal.Length = median(Petal.Length), Petal.Width = median(Petal.Width)),
by=.(Species)][order(-Species)]
## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1: virginica 6.5 3.0 5.55 2.0
## 2: versicolor 5.9 2.8 4.35 1.3
## 3: setosa 5.0 3.4 1.50 0.2
# Mean of columns
# DT[ , lapply(.SD, FUN=mean), by=.(x)]
# Median of columns
# DT[ , lapply(.SD, FUN=median), by=.(x)]
# Calculate the sum of the Q columns
# DT[ , lapply(.SD, FUN=sum), , .SDcols=2:4]
# Calculate the sum of columns H1 and H2
# DT[ , lapply(.SD, FUN=sum), , .SDcols=paste0("H", 1:2)]
# Select all but the first row of groups 1 and 2, returning only the grp column and the Q columns
# foo = function(x) { x[-1] }
# DT[ , lapply(.SD, FUN=foo), by=.(grp), .SDcols=paste0("Q", 1:3)]
# Sum of all columns and the number of rows
# DT[, c(lapply(.SD, FUN=sum), .N), by=.(x), .SDcols=names(DT)]
# Cumulative sum of column x and y while grouping by x and z > 8
# DT[, lapply(.SD, FUN=cumsum), by=.(by1=x, by2=(z>8)), .SDcols=c("x", "y")]
# Chaining
# DT[, lapply(.SD, FUN=cumsum), by=.(by1=x, by2=(z>8)), .SDcols=c("x", "y")][ , lapply(.SD, FUN=max), by=.(by1), .SDcols=c("x", "y")]
# The data.table DT
DT <- data.table(A = letters[c(1, 1, 1, 2, 2)], B = 1:5)
str(DT)
## Classes 'data.table' and 'data.frame': 5 obs. of 2 variables:
## $ A: chr "a" "a" "a" "b" ...
## $ B: int 1 2 3 4 5
## - attr(*, ".internal.selfref")=<externalptr>
DT
## A B
## 1: a 1
## 2: a 2
## 3: a 3
## 4: b 4
## 5: b 5
# Add column by reference: Total
DT[ , Total:=sum(B), by=.(A)]
DT
## A B Total
## 1: a 1 6
## 2: a 2 6
## 3: a 3 6
## 4: b 4 9
## 5: b 5 9
# Add 1 to column B
DT[c(2,4) , B:=B+1L, ]
DT
## A B Total
## 1: a 1 6
## 2: a 3 6
## 3: a 3 6
## 4: b 5 9
## 5: b 5 9
# Add a new column Total2
DT[2:4, Total2:=sum(B), by=.(A)]
DT
## A B Total Total2
## 1: a 1 6 NA
## 2: a 3 6 6
## 3: a 3 6 6
## 4: b 5 9 5
## 5: b 5 9 NA
# Remove the Total column
DT[ , Total := NULL, ]
DT
## A B Total2
## 1: a 1 NA
## 2: a 3 6
## 3: a 3 6
## 4: b 5 5
## 5: b 5 NA
# Select the third column using `[[`
DT[[3]]
## [1] NA 6 6 5 NA
# A data.table DT has been created for you
DT <- data.table(A = c(1, 1, 1, 2, 2), B = 1:5)
str(DT)
## Classes 'data.table' and 'data.frame': 5 obs. of 2 variables:
## $ A: num 1 1 1 2 2
## $ B: int 1 2 3 4 5
## - attr(*, ".internal.selfref")=<externalptr>
DT
## A B
## 1: 1 1
## 2: 1 2
## 3: 1 3
## 4: 2 4
## 5: 2 5
# Update B, add C and D
DT[ , c("B", "C", "D") := .(B + 1, A + B, 2), ]
DT
## A B C D
## 1: 1 2 2 2
## 2: 1 3 3 2
## 3: 1 4 4 2
## 4: 2 5 6 2
## 5: 2 6 7 2
# Delete my_cols
my_cols <- c("B", "C")
DT[ , (my_cols) := NULL, ]
DT
## A D
## 1: 1 2
## 2: 1 2
## 3: 1 2
## 4: 2 2
## 5: 2 2
# Delete column 2 by number
DT[[2]] <- NULL
DT
## A
## 1: 1
## 2: 1
## 3: 1
## 4: 2
## 5: 2
# Set the seed
# set.seed(1)
# Check the DT that is made available to you
# DT
# For loop with set
# for(i in 2:4) { set(DT, sample(nrow(DT), 3), i, NA) }
# Change the column names to lowercase
# setnames(DT, letters[1:4])
# Print the resulting DT to the console
# DT
# Define DT
DT <- data.table(a = letters[c(1, 1, 1, 2, 2)], b = 1)
str(DT)
## Classes 'data.table' and 'data.frame': 5 obs. of 2 variables:
## $ a: chr "a" "a" "a" "b" ...
## $ b: num 1 1 1 1 1
## - attr(*, ".internal.selfref")=<externalptr>
DT
## a b
## 1: a 1
## 2: a 1
## 3: a 1
## 4: b 1
## 5: b 1
# Add a suffix "_2" to all column names
setnames(DT, paste0(names(DT), "_2"))
DT
## a_2 b_2
## 1: a 1
## 2: a 1
## 3: a 1
## 4: b 1
## 5: b 1
# Change column name "a_2" to "A2"
setnames(DT, "a_2", "A2")
DT
## A2 b_2
## 1: a 1
## 2: a 1
## 3: a 1
## 4: b 1
## 5: b 1
# Reverse the order of the columns
setcolorder(DT, 2:1)
DT
## b_2 A2
## 1: 1 a
## 2: 1 a
## 3: 1 a
## 4: 1 b
## 5: 1 b
Example code includes:
# iris as a data.table
iris <- as.data.table(iris)
# Remove the "Sepal." prefix
names(iris) <- gsub("Sepal\\.", "", names(iris))
# Remove the two columns starting with "Petal"
iris[, c("Petal.Length", "Petal.Width") := NULL, ]
# Cleaned up iris data.table
str(iris)
## Classes 'data.table' and 'data.frame': 150 obs. of 3 variables:
## $ Length : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Species: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, ".internal.selfref")=<externalptr>
# Area is greater than 20 square centimeters
iris[ Width * Length > 20 ]
## Length Width Species
## 1: 5.4 3.9 setosa
## 2: 5.8 4.0 setosa
## 3: 5.7 4.4 setosa
## 4: 5.4 3.9 setosa
## 5: 5.7 3.8 setosa
## 6: 5.2 4.1 setosa
## 7: 5.5 4.2 setosa
## 8: 7.0 3.2 versicolor
## 9: 6.4 3.2 versicolor
## 10: 6.9 3.1 versicolor
## 11: 6.3 3.3 versicolor
## 12: 6.7 3.1 versicolor
## 13: 6.7 3.0 versicolor
## 14: 6.0 3.4 versicolor
## 15: 6.7 3.1 versicolor
## 16: 6.3 3.3 virginica
## 17: 7.1 3.0 virginica
## 18: 7.6 3.0 virginica
## 19: 7.3 2.9 virginica
## 20: 7.2 3.6 virginica
## 21: 6.5 3.2 virginica
## 22: 6.8 3.0 virginica
## 23: 6.4 3.2 virginica
## 24: 7.7 3.8 virginica
## 25: 7.7 2.6 virginica
## 26: 6.9 3.2 virginica
## 27: 7.7 2.8 virginica
## 28: 6.7 3.3 virginica
## 29: 7.2 3.2 virginica
## 30: 7.2 3.0 virginica
## 31: 7.4 2.8 virginica
## 32: 7.9 3.8 virginica
## 33: 7.7 3.0 virginica
## 34: 6.3 3.4 virginica
## 35: 6.9 3.1 virginica
## 36: 6.7 3.1 virginica
## 37: 6.9 3.1 virginica
## 38: 6.8 3.2 virginica
## 39: 6.7 3.3 virginica
## 40: 6.7 3.0 virginica
## 41: 6.2 3.4 virginica
## Length Width Species
# Add new boolean column
iris[, is_large := Width * Length > 25]
## Warning in `[.data.table`(iris, , `:=`(is_large, Width * Length > 25)):
## Invalid .internal.selfref detected and fixed by taking a (shallow) copy
## of the data.table so that := can add this new column by reference. At
## an earlier point, this data.table has been copied by R (or been created
## manually using structure() or similar). Avoid key<-, names<- and attr<-
## which in R currently (and oddly) may copy the whole data.table. Use set*
## syntax instead to avoid copying: ?set, ?setnames and ?setattr. Also, in
## R<=v3.0.2, list(DT1,DT2) copied the entire DT1 and DT2 (R's list() used to
## copy named objects); please upgrade to R>v3.0.2 if that is biting. If this
## message doesn't help, please report to datatable-help so the root cause can
## be fixed.
# Now large observations with is_large
iris[is_large == TRUE]
## Length Width Species is_large
## 1: 5.7 4.4 setosa TRUE
## 2: 7.2 3.6 virginica TRUE
## 3: 7.7 3.8 virginica TRUE
## 4: 7.9 3.8 virginica TRUE
iris[(is_large)] # Also OK
## Length Width Species is_large
## 1: 5.7 4.4 setosa TRUE
## 2: 7.2 3.6 virginica TRUE
## 3: 7.7 3.8 virginica TRUE
## 4: 7.9 3.8 virginica TRUE
# The 'keyed' data.table DT
DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9, 8, 8, 6),
C = 6:12)
setkey(DT, A, B)
str(DT)
## Classes 'data.table' and 'data.frame': 7 obs. of 3 variables:
## $ A: chr "a" "a" "b" "b" ...
## $ B: num 4 8 1 5 8 6 9
## $ C: int 7 10 8 6 11 12 9
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr "A" "B"
DT
## A B C
## 1: a 4 7
## 2: a 8 10
## 3: b 1 8
## 4: b 5 6
## 5: b 8 11
## 6: c 6 12
## 7: c 9 9
# Select the "b" group
DT["b"]
## A B C
## 1: b 1 8
## 2: b 5 6
## 3: b 8 11
# "b" and "c" groups
DT[c("b", "c")]
## A B C
## 1: b 1 8
## 2: b 5 6
## 3: b 8 11
## 4: c 6 12
## 5: c 9 9
# The first row of the "b" and "c" groups
DT[c("b", "c"), mult = "first"]
## A B C
## 1: b 1 8
## 2: c 6 12
# First and last row of the "b" and "c" groups
DT[c("b", "c"), .SD[c(1, .N)], by = .EACHI]
## A B C
## 1: b 1 8
## 2: b 8 11
## 3: c 6 12
## 4: c 9 9
# Copy and extend code for instruction 4: add printout
DT[c("b", "c"), { print(.SD); .SD[c(1, .N)] }, by = .EACHI]
## B C
## 1: 1 8
## 2: 5 6
## 3: 8 11
## B C
## 1: 6 12
## 2: 9 9
## A B C
## 1: b 1 8
## 2: b 8 11
## 3: c 6 12
## 4: c 9 9
# Keyed data.table DT
DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9, 8, 8, 6),
C = 6:12,
key = "A,B")
str(DT)
## Classes 'data.table' and 'data.frame': 7 obs. of 3 variables:
## $ A: chr "a" "a" "b" "b" ...
## $ B: num 4 8 1 5 8 6 9
## $ C: int 7 10 8 6 11 12 9
## - attr(*, "sorted")= chr "A" "B"
## - attr(*, ".internal.selfref")=<externalptr>
DT
## A B C
## 1: a 4 7
## 2: a 8 10
## 3: b 1 8
## 4: b 5 6
## 5: b 8 11
## 6: c 6 12
## 7: c 9 9
# Get the key of DT
key(DT)
## [1] "A" "B"
# Row where A == "b" and B == 6
DT[.("b", 6)]
## A B C
## 1: b 6 NA
# Return the prevailing row
DT[.("b", 6), roll=TRUE]
## A B C
## 1: b 6 6
# Return the nearest row
DT[.("b", 6), roll="nearest"]
## A B C
## 1: b 6 6
# Keyed data.table DT
DT <- data.table(A = letters[c(2, 1, 2, 3, 1, 2, 3)],
B = c(5, 4, 1, 9, 8, 8, 6),
C = 6:12,
key = "A,B")
str(DT)
## Classes 'data.table' and 'data.frame': 7 obs. of 3 variables:
## $ A: chr "a" "a" "b" "b" ...
## $ B: num 4 8 1 5 8 6 9
## $ C: int 7 10 8 6 11 12 9
## - attr(*, "sorted")= chr "A" "B"
## - attr(*, ".internal.selfref")=<externalptr>
DT
## A B C
## 1: a 4 7
## 2: a 8 10
## 3: b 1 8
## 4: b 5 6
## 5: b 8 11
## 6: c 6 12
## 7: c 9 9
# Print the sequence (-2):10 for the "b" group
DT[.("b", (-2):10)]
## A B C
## 1: b -2 NA
## 2: b -1 NA
## 3: b 0 NA
## 4: b 1 8
## 5: b 2 NA
## 6: b 3 NA
## 7: b 4 NA
## 8: b 5 6
## 9: b 6 NA
## 10: b 7 NA
## 11: b 8 11
## 12: b 9 NA
## 13: b 10 NA
# Add code: carry the prevailing values forwards
DT[.("b", (-2):10), roll=TRUE]
## A B C
## 1: b -2 NA
## 2: b -1 NA
## 3: b 0 NA
## 4: b 1 8
## 5: b 2 8
## 6: b 3 8
## 7: b 4 8
## 8: b 5 6
## 9: b 6 6
## 10: b 7 6
## 11: b 8 11
## 12: b 9 11
## 13: b 10 11
# Add code: carry the first observation backwards
DT[.("b", (-2):10), roll=TRUE, rollends=TRUE]
## A B C
## 1: b -2 8
## 2: b -1 8
## 3: b 0 8
## 4: b 1 8
## 5: b 2 8
## 6: b 3 8
## 7: b 4 8
## 8: b 5 6
## 9: b 6 6
## 10: b 7 6
## 11: b 8 11
## 12: b 9 11
## 13: b 10 11
Jeff Ryan, the creator of quantmod and organizer of the R/Finance conference, has developed xts and zoo to simplify working with time series data. The course will cover five areas (chapters):
“xts” stands for extensible time series. The core of each “xts” is a “zoo” object, consisting of a matrix plus an index.
There are a few special behaviors of xts:
The “xts” object can be de-constructed when needed:
Data usually already exists and needs to be “wrangled” in to a proper format for xts/zoo. The easiest way to convert is using as.xts(). You can coerce truly external data after loading it, and can also save data with Can also save with write.zoo(x, “file”).
Subsetting based on time is a particular strength of xts. xts supports ISO8601:2004 (the standard, “right way”, to unambiguously consider times):
xts allows for four methods of specifying dates or intervals:
Can also use some traditional R-like methods (since xts extends zoo, and zoo extends base R):
Can set the flag which.i = TRUE to get back the correct records (row numbers). For example, index <- x[“2007-06-26/2007-06-28”, which.i = TRUE].
Description of key behaviors when working with an xts object:
xts introduces a few relatives of the head() and tail() functionality. These are the first() and last() functions.
Math operations using xts - xts is a matrix - need to be careful about matrix operations. Math operations are run only on the intersection of items:
Merging time series is common. Merge (cbind, merge) combines by columns, but joining based on index.
Merge (rbind( combine by rows, though all rows must already have an index. Basically, the rbind MUST be used on a time series.
Missing data is common, and xts inherits all of the zoo methods for dealing with missing data. The locf is the “last observation carry forward” (latest value that is not NA) - called with na.locf:
The NA can be managed in several ways:
Lag operators and difference operations. Seasonality is a repeating pattern. There is often a need to compare seasonality – for example, compare Mondays. Stationarity refers to some bound of the series.
The lag() function will change the timestamp, so that (for example) today can be merged as last week:
The “one period lag first difference” is calculated as diff(x, lag=1, differences=1, arithmetic=TRUE, log=FALSE, na.pad=TRUE, . ).
There are two main approaches for applying functions on discrete periods or intervals:
Time series aggregation can also be handled by xts:
Time series data can also be managed in a “rolling” manner - discrete or continuous:
Internals of xts such as indices and timezones:
Final topics:
Example code includes:
library(xts)
## Warning: package 'xts' was built under R version 3.2.5
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'xts'
## The following object is masked from 'package:data.table':
##
## last
## The following objects are masked from 'package:dplyr':
##
## first, last
library(zoo)
x <- matrix(data=1:4, ncol=2)
idx <- as.Date(c("2015-01-01", "2015-02-01"))
# Create the xts
X <- xts(x, order.by = idx)
# Decosntruct the xts
coredata(X, fmt=FALSE)
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
index(X)
## [1] "2015-01-01" "2015-02-01"
# Working with the sunspots data
data(sunspots)
class(sunspots)
## [1] "ts"
sunspots_xts <- as.xts(sunspots)
class(sunspots_xts)
## [1] "xts" "zoo"
head(sunspots_xts)
## [,1]
## Jan 1749 58.0
## Feb 1749 62.6
## Mar 1749 70.0
## Apr 1749 55.7
## May 1749 85.0
## Jun 1749 83.5
# Example from chapter #1
ex_matrix <- xts(matrix(data=c(1, 1, 1, 2, 2, 2), ncol=2),
order.by=as.Date(c("2016-06-01", "2016-06-02", "2016-06-03"))
)
core <- coredata(ex_matrix)
# View the structure of ex_matrix
str(ex_matrix)
## An 'xts' object on 2016-06-01/2016-06-03 containing:
## Data: num [1:3, 1:2] 1 1 1 2 2 2
## Indexed by objects of class: [Date] TZ: UTC
## xts Attributes:
## NULL
# Extract the 3rd observation of the 2nd column of ex_matrix
ex_matrix[3, 2]
## [,1]
## 2016-06-03 2
# Extract the 3rd observation of the 2nd column of core
core[3, 2]
## [1] 2
# Create the object data using 5 random numbers
data <- rnorm(5)
# Create dates as a Date class object starting from 2016-01-01
dates <- seq(as.Date("2016-01-01"), length = 5, by = "days")
# Use xts() to create smith
smith <- xts(x = data, order.by = dates)
# Create bday (1899-05-08) using a POSIXct date class object
bday <- as.POSIXct("1899-05-08")
# Create hayek and add a new attribute called born
hayek <- xts(x = data, order.by = dates, born = bday)
# Extract the core data of hayek
hayek_core <- coredata(hayek)
# View the class of hayek_core
class(hayek_core)
## [1] "matrix"
# Extract the index of hayek
hayek_index <- index(hayek)
# View the class of hayek_index
class(hayek_index)
## [1] "Date"
# Create dates
dates <- as.Date("2016-01-01") + 0:4
# Create ts_a
ts_a <- xts(x = 1:5, order.by = dates)
# Create ts_b
ts_b <- xts(x = 1:5, order.by = as.POSIXct(dates))
# Extract the rows of ts_a using the index of ts_b
ts_a[index(ts_b)]
## [,1]
## 2016-01-01 1
## 2016-01-02 2
## 2016-01-03 3
## 2016-01-04 4
## 2016-01-05 5
# Extract the rows of ts_b using the index of ts_a
ts_b[index(ts_a)]
## [,1]
data(austres)
# Convert austres to an xts object called au
au <- as.xts(austres)
# Convert your xts object (au) into a matrix am
am <- as.matrix(au)
# Convert the original austres into a matrix am2
am2 <- as.matrix(austres)
# Create dat by reading tmp_file
tmp_file <- "http://s3.amazonaws.com/assets.datacamp.com/production/course_1127/datasets/tmp_file.csv"
dat <- read.csv(tmp_file)
# Convert dat into xts
xts(dat, order.by = as.Date(rownames(dat), "%m/%d/%Y"))
## a b
## 2015-01-02 1 3
## 2015-02-03 2 4
# Read tmp_file using read.zoo
dat_zoo <- read.zoo(tmp_file, index.column = 0, sep = ",", format = "%m/%d/%Y")
# Convert dat_zoo to xts
dat_xts <- as.xts(dat_zoo)
# Convert sunspots to xts using as.xts(). Save this as sunspots_xts
sunspots_xts <- as.xts(sunspots)
# Get the temporary file name
tmp <- tempfile()
# Write the xts object using zoo to tmp
write.zoo(sunspots_xts, sep = ",", file = tmp)
# Read the tmp file. FUN = as.yearmon converts strings such as Jan 1749 into a proper time class
sun <- read.zoo(tmp, sep = ",", FUN = as.yearmon)
# Convert sun into xts. Save this as sun_xts
sun_xts <- as.xts(sun)
data(edhec, package="PerformanceAnalytics")
head(edhec["2007-01", 1])
## Convertible Arbitrage
## 2007-01-31 0.013
head(edhec["2007-01/2007-03", 1])
## Convertible Arbitrage
## 2007-01-31 0.0130
## 2007-02-28 0.0117
## 2007-03-31 0.0060
head(edhec["200701/03", 1])
## Convertible Arbitrage
## 2007-01-31 0.0130
## 2007-02-28 0.0117
## 2007-03-31 0.0060
first(edhec[, "Funds of Funds"], "4 months")
## Funds of Funds
## 1997-01-31 0.0317
## 1997-02-28 0.0106
## 1997-03-31 -0.0077
## 1997-04-30 0.0009
last(edhec[, "Funds of Funds"], "1 year")
## Funds of Funds
## 2009-01-31 0.0060
## 2009-02-28 -0.0037
## 2009-03-31 0.0008
## 2009-04-30 0.0092
## 2009-05-31 0.0312
## 2009-06-30 0.0024
## 2009-07-31 0.0153
## 2009-08-31 0.0113
Data visulaization is the combination of Statistics and Design:
The Anscombe plot examples show four different datasets explained by the identical linear model. This reinforces the importance of plotting the data prior to running analyses and drawing conclusions.
The “Grammar of Graphics” is a plotting framework based on the book by Leland Wilkinson, “Grammar of Graphics” 2(1999). The gist is that graphics are made of distinct layers of grammatical elements. Meaningful plots are created through aesthetic mapping.
Essential Grammatical Elements include:
The ggplot2 package was one of the first developed and designed by Hadley Wickham. It implements the “Grammar of Graphics” in R, for example with:
The Anscombe data is good to have plotted for reference:
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.2.5
data(anscombe)
ansX <- with(anscombe, c(x1, x2, x3, x4))
ansY <- with(anscombe, c(y1, y2, y3, y4))
ansType <- rep(1:4, each=nrow(anscombe))
ansFrame <- data.frame(x=ansX, y=ansY, series=factor(ansType))
# ggplot example for Anscombe data
ggplot(ansFrame, aes(x=x, y=y)) +
geom_point() +
geom_smooth(method="lm", col="red", se=FALSE, fullrange=TRUE) +
facet_wrap(~ series, nrow=2)
As well, the basic example code from above is useful to explore:
data(iris)
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) +
geom_jitter(alpha = 0.6) +
facet_grid(. ~ Species) +
stat_smooth(method = "lm", se = FALSE, col="red")
Some additional basic ggplot syntax includes (cached, since plotting each point of the diamonds dataset is taxing for the graphics):
# Explore the mtcars data frame with str()
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# Execute the following command
ggplot(mtcars, aes(x = cyl, y = mpg)) +
geom_point()
# Change the command below so that cyl is treated as factor
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_point()
# A scatter plot has been made for you
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point()
# Replace ___ with the correct vector
ggplot(mtcars, aes(x = wt, y = mpg, col = disp)) +
geom_point()
# Replace ___ with the correct vector
ggplot(mtcars, aes(x = wt, y = mpg, size = disp)) +
geom_point()
# Explore the diamonds data frame with str()
data(diamonds)
str(diamonds)
## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
# Add geom_point() with +
ggplot(diamonds, aes(x = carat, y = price)) + geom_point()
# Add geom_point() and geom_smooth() with +
ggplot(diamonds, aes(x = carat, y = price)) + geom_point() + geom_smooth()
# The plot you created in the previous exercise
ggplot(diamonds, aes(x = carat, y = price)) +
geom_point() +
geom_smooth()
# Copy the above command but show only the smooth line
ggplot(diamonds, aes(x = carat, y = price)) +
geom_smooth()
# Copy the above command and assign the correct value to col in aes()
ggplot(diamonds, aes(x = carat, y = price, col=clarity)) +
geom_smooth()
# Keep the color settings from previous command. Plot only the points with argument alpha.
ggplot(diamonds, aes(x = carat, y = price, col=clarity)) +
geom_point(alpha = 0.4)
# Create the object containing the data and aes layers: dia_plot
dia_plot <- ggplot(diamonds, aes(x = carat, y=price))
# Add a geom layer with + and geom_point()
dia_plot + geom_point()
# Add the same geom layer, but with aes() inside
dia_plot + geom_point(aes(col = clarity))
set.seed(1)
# The dia_plot object has been created for you
dia_plot <- ggplot(diamonds, aes(x = carat, y = price))
# Expand dia_plot by adding geom_point() with alpha set to 0.2
dia_plot <- dia_plot + geom_point(alpha = 0.2)
# Plot dia_plot with additional geom_smooth() with se set to FALSE
dia_plot + geom_smooth(se = FALSE)
# Copy the command from above and add aes() with the correct mapping to geom_smooth()
dia_plot + geom_smooth(se = FALSE, aes(col = clarity))
Data Layer - How data structure influences plots (ggplot2 vs. base):
Can add some additional points (similar to points() in base, but with axes rescaling for you):
Example code includes:
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# Plot the correct variables of mtcars
plot(mtcars$wt, mtcars$mpg, col=mtcars$cyl)
# Change cyl inside mtcars to a factor
mtcars$cyl <- as.factor(mtcars$cyl)
# Make the same plot as in the first instruction
plot(mtcars$wt, mtcars$mpg, col=mtcars$cyl)
# Use lm() to calculate a linear model and save it as carModel
carModel <- lm(mpg ~ wt, data = mtcars)
# Call abline() with carModel as first argument and set lty to 2
abline(carModel, lty=2)
# Plot each subset efficiently with lapply
# You don't have to edit this code
lapply(mtcars$cyl, function(x) {
abline(lm(mpg ~ wt, mtcars, subset = (cyl == x)), col = x)
})
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# This code will draw the legend of the plot
# You don't have to edit this code
legend(x = 5, y = 33, legend = levels(mtcars$cyl),
col = 1:3, pch = 1, bty = "n")
# Plot 1: add geom_point() to this command to create a scatter plot
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +
geom_point() # Fill in using instructions Plot 1
# Plot 2: include the lines of the linear models, per cyl
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +
geom_point() + # Copy from Plot 1
geom_smooth(method="lm", se=FALSE) # Fill in using instructions Plot 2
# Plot 3: include a lm for the entire dataset in its whole
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +
geom_point() + # Copy from Plot 2
geom_smooth(method="lm", se=FALSE) + # Copy from Plot 2
geom_smooth(aes(group = 1), method="lm", se=FALSE, linetype = 2) # Fill in using instructions Plot 3
data(iris)
str(iris)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
# Option 1
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point() +
geom_point(aes(x = Petal.Length, y = Petal.Width), col = "red")
# DS code to match up to lecturer data set formats
data(iris)
longIris <- tidyr::gather(iris, Type, Measure, -Species)
intIris <- tidyr::separate(longIris, Type, c("Part", "Metric"))
intIris$rowNum <- c(1:150, 1:150, 151:300, 151:300)
iris.wide <- tidyr::spread(intIris, Metric, Measure)
iris.tidy <- dplyr::select(dplyr::mutate(intIris, Value=Measure, Measure=Metric), Species, Part, Measure, Value)
# Option 2
ggplot(iris.wide, aes(x = Length, y = Width, col = Part)) +
geom_point()
# Consider the structure of iris, iris.wide and iris.tidy (in that order)
str(iris)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
str(iris.wide)
## 'data.frame': 300 obs. of 5 variables:
## $ Species: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Part : chr "Petal" "Petal" "Petal" "Petal" ...
## $ rowNum : int 151 152 153 154 155 156 157 158 159 160 ...
## $ Length : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
str(iris.tidy)
## 'data.frame': 600 obs. of 4 variables:
## $ Species: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Part : chr "Sepal" "Sepal" "Sepal" "Sepal" ...
## $ Measure: chr "Length" "Length" "Length" "Length" ...
## $ Value : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
# Think about which dataset you would use to get the plot shown right
# Fill in the ___ to produce the plot given to the right
ggplot(iris.tidy, aes(x = Species, y = Value, col = Part)) +
geom_jitter() +
facet_grid(. ~ Measure)
# Load the tidyr package
library(tidyr)
# Fill in the ___ to produce to the correct iris.tidy dataset
iris.tidy <- iris %>%
gather(key, Value, -Species) %>%
separate(key, c("Part", "Measure"), "\\.")
# Consider the head of iris, iris.wide and iris.tidy (in that order)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
head(iris.wide)
## Species Part rowNum Length Width
## 1 setosa Petal 151 1.4 0.2
## 2 setosa Petal 152 1.4 0.2
## 3 setosa Petal 153 1.3 0.2
## 4 setosa Petal 154 1.5 0.2
## 5 setosa Petal 155 1.4 0.2
## 6 setosa Petal 156 1.7 0.4
head(iris.tidy)
## Species Part Measure Value
## 1 setosa Sepal Length 5.1
## 2 setosa Sepal Length 4.9
## 3 setosa Sepal Length 4.7
## 4 setosa Sepal Length 4.6
## 5 setosa Sepal Length 5.0
## 6 setosa Sepal Length 5.4
# Think about which dataset you would use to get the plot shown right
# Fill in the ___ to produce the plot given to the right
ggplot(iris.wide, aes(x = Length, y = Width, col = Part)) +
geom_jitter() +
facet_grid(. ~ Species)
# Add column with unique ids (don't need to change)
iris$Flower <- 1:nrow(iris)
# Fill in the ___ to produce to the correct iris.wide dataset
iris.wide <- iris %>%
gather(key, value, -Species, -Flower) %>%
separate(key, c("Part", "Measure"), "\\.") %>%
spread(Measure, value)
Visible aesthetics are the cornerstone of the ggplot:
Modifying aesthetics:
Best practices for choosing among the aesthetics (though note that “there is a fair bit of creativity involved”):
Example code includes:
data(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$am <- as.factor(mtcars$am)
# Map cyl to y
ggplot(mtcars, aes(x=mpg, y=cyl)) + geom_point()
# Map cyl to x
ggplot(mtcars, aes(y=mpg, x=cyl)) + geom_point()
# Map cyl to col
ggplot(mtcars, aes(y=mpg, x=wt, col=cyl)) + geom_point()
# Change shape and size of the points in the above plot
ggplot(mtcars, aes(y=mpg, x=wt, col=cyl)) + geom_point(shape=1, size=4)
# Map cyl to fill
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) +
geom_point()
# Change shape, size and alpha of the points in the above plot
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) +
geom_point(shape=16, size=6, alpha=0.6)
# Map cyl to size
ggplot(mtcars, aes(y=mpg, x=wt, size=cyl)) + geom_point()
## Warning: Using size for a discrete variable is not advised.
# Map cyl to alpha
ggplot(mtcars, aes(y=mpg, x=wt, alpha=cyl)) + geom_point()
# Map cyl to shape
ggplot(mtcars, aes(y=mpg, x=wt, shape=cyl)) + geom_point()
# Map cyl to labels
ggplot(mtcars, aes(y=mpg, x=wt, label=cyl)) + geom_text()
# Define a hexadecimal color
my_color <- "#123456"
# Set the color aesthetic
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point()
# Set the color aesthetic and attribute
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(col = my_color)
# Set the fill aesthetic and color, size and shape attributes
ggplot(mtcars, aes(x=wt, y=mpg, fill=cyl)) + geom_point(size=10, shape=23, col=my_color)
# Expand to draw points with alpha 0.5
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_point(alpha=0.5)
# Expand to draw points with shape 24 and color yellow
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_point(shape=24, col="yellow")
# Expand to draw text with label x, color red and size 10
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_text(label="x", col="red", size=10)
# Map mpg onto x, qsec onto y and factor(cyl) onto col
ggplot(mtcars, aes(x=mpg, y=qsec, col=factor(cyl))) + geom_point()
# Add mapping: factor(am) onto shape
ggplot(mtcars, aes(x=mpg, y=qsec, col=factor(cyl), shape=factor(am))) + geom_point()
# Add mapping: (hp/wt) onto size
ggplot(mtcars, aes(x=mpg, y=qsec, col=factor(cyl), shape=factor(am), size=(hp/wt))) + geom_point()
# Basic scatter plot: wt on x-axis and mpg on y-axis; map cyl to col
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(size=4)
# Hollow circles - an improvement
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(size=4, shape=1)
# Add transparency - very nice
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(size=4, alpha=0.6)
Next, bar plots are examined using the same data:
cyl.am <- ggplot(mtcars, aes(x = factor(cyl), fill = factor(am)))
# Add geom (position = "stack" by default)
cyl.am + geom_bar()
# Fill - show proportion
cyl.am +
geom_bar(position = "fill")
# Dodging - principles of similarity and proximity
cyl.am +
geom_bar(position = "dodge")
# Clean up the axes with scale_ functions
val = c("#E41A1C", "#377EB8")
lab = c("Manual", "Automatic")
cyl.am +
geom_bar(position = "dodge") +
scale_x_discrete("Cylinders") +
scale_y_continuous("Number") +
scale_fill_manual("Transmission",
values = val,
labels = lab)
# Add a new column called group
mtcars$group <- 0
# Create jittered plot of mtcars: mpg onto x, group onto y
ggplot(mtcars, aes(x = mpg, y=group)) + geom_jitter()
# Change the y aesthetic limits
ggplot(mtcars, aes(x = mpg, y=group)) + geom_jitter() + scale_y_continuous(limits = c(-2, 2))
Further, the diamonds data set is explored to show techniques for minimizing over-plotting problems. Per previous, it is cached due to the lengthy plot times driven by the many data points:
data(diamonds)
str(diamonds)
## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
# Scatter plot: carat (x), price (y), clarity (col)
ggplot(diamonds, aes(x=carat, y=price, col=clarity)) + geom_point()
# Adjust for overplotting
ggplot(diamonds, aes(x=carat, y=price, col=clarity)) + geom_point(alpha = 0.5)
# Scatter plot: clarity (x), carat (y), price (col)
ggplot(diamonds, aes(y=carat, x=clarity, col=price)) + geom_point(alpha = 0.5)
# Dot plot with jittering
ggplot(diamonds, aes(y=carat, x=clarity, col=price)) + geom_point(alpha = 0.5, position="jitter")
The geometries layer includes the most common plot types:
Scatter plots examples - geom_point(), geom_jitter(), geom_abline():
Bar plots examples - histogram, bar, errorbar:
Line plots examples - line:
Example code from mtcars includes:
# mtcars point plots
# Plot the cyl on the x-axis and wt on the y-axis
ggplot(mtcars, aes(x=cyl, y=wt)) + geom_point()
# Use geom_jitter() instead of geom_point()
ggplot(mtcars, aes(x=cyl, y=wt)) + geom_jitter()
# Define the position object using position_jitter(): posn.j
posn.j <- position_jitter(width = 0.1)
# Use posn.j in geom_point()
ggplot(mtcars, aes(x=cyl, y=wt)) + geom_point(position = posn.j)
# mtcars bar plots
# Make a univariate histogram
ggplot(mtcars, aes(x=mpg)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Change the bin width to 1
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 1)
# Change the y aesthetic to density
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 1, aes(y=..density..))
# Custom color code
myBlue <- "#377EB8"
# Change the fill color to myBlue
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 1, aes(y=..density..), fill=myBlue)
# Draw a bar plot of cyl, filled according to am
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar()
# Change the position argument to stack
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position="stack")
# Change the position argument to fill
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position="fill")
# Change the position argument to dodge
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position="dodge")
# Draw a bar plot of cyl, filled according to am
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar()
# Change the position argument to "dodge"
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position = "dodge")
# Define posn_d with position_dodge()
posn_d <- position_dodge(width=0.2)
# Change the position argument to posn_d
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position = posn_d)
# Use posn_d as position and adjust alpha to 0.6
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position = posn_d, alpha=0.6)
# A basic histogram, add coloring defined by cyl
ggplot(mtcars, aes(x=mpg, fill=cyl)) +
geom_histogram(binwidth = 1)
# Change position to identity
ggplot(mtcars, aes(x=mpg, fill=cyl)) +
geom_histogram(binwidth = 1, position="identity")
# Change geom to freqpoly (position is identity by default)
ggplot(mtcars, aes(x=mpg, col=cyl)) +
geom_freqpoly(binwidth = 1, position="identity")
# Example of how to use a brewed color palette
ggplot(mtcars, aes(x = cyl, fill = am)) +
geom_bar() +
scale_fill_brewer(palette = "Set1")
# Basic histogram plot command
ggplot(mtcars, aes(mpg)) +
geom_histogram(binwidth = 1)
# Expand the histogram to fill using am
ggplot(mtcars, aes(x=mpg, fill=am)) +
geom_histogram(binwidth = 1)
# Change the position argument to "dodge"
ggplot(mtcars, aes(x=mpg, fill=am)) +
geom_histogram(binwidth = 1, position="dodge")
# Change the position argument to "fill"
ggplot(mtcars, aes(x=mpg, fill=am)) +
geom_histogram(binwidth = 1, position="fill")
## Warning: Removed 16 rows containing missing values (geom_bar).
# Change the position argument to "identity" and set alpha to 0.4
ggplot(mtcars, aes(x=mpg, fill=am)) +
geom_histogram(binwidth = 1, position="identity", alpha = 0.4)
# Change fill to cyl
ggplot(mtcars, aes(x=mpg, fill=cyl)) +
geom_histogram(binwidth = 1, position="identity", alpha = 0.4)
Next, a few examples are run from dataset car::Vocab (cached due to plotting size/time):
Vocab <- car::Vocab
str(Vocab)
## 'data.frame': 21638 obs. of 4 variables:
## $ year : int 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 ...
## $ sex : Factor w/ 2 levels "Female","Male": 1 1 2 1 2 2 1 2 2 1 ...
## $ education : int 9 14 14 17 14 14 12 10 11 9 ...
## $ vocabulary: int 3 6 9 8 1 7 6 6 5 1 ...
# Basic scatter plot of vocabulary (y) against education (x). Use geom_point()
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_point()
# Use geom_jitter() instead of geom_point()
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_jitter()
# Using the above plotting command, set alpha to a very low 0.2
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_jitter(alpha = 0.2)
# Using the above plotting command, set the shape to 1
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_jitter(alpha = 0.2, shape=1)
# Plot education on x and vocabulary on fill
# Use the default brewed color palette
ggplot(Vocab, aes(x = education, fill = vocabulary)) +
geom_bar(position="fill") +
scale_fill_brewer()
# Definition of a set of blue colors
blues <- brewer.pal(9, "Blues")
# Make a color range using colorRampPalette() and the set of blues
blue_range <- colorRampPalette(blues)
# Use blue_range to adjust the color of the bars, use scale_fill_manual()
ggplot(Vocab, aes(x = education, fill = factor(vocabulary))) +
geom_bar(position = "fill") +
scale_fill_manual(values = blue_range(11))
Lastly, a few additional plots are displayed:
# Print out head of economics
data(economics)
head(economics)
## # A tibble: 6 × 6
## date pce pop psavert uempmed unemploy
## <date> <dbl> <int> <dbl> <dbl> <int>
## 1 1967-07-01 507.4 198712 12.5 4.5 2944
## 2 1967-08-01 510.5 198911 12.5 4.7 2945
## 3 1967-09-01 516.3 199113 11.7 4.6 2958
## 4 1967-10-01 512.9 199311 12.5 4.9 3143
## 5 1967-11-01 518.1 199498 12.5 4.7 3066
## 6 1967-12-01 525.8 199657 12.1 4.8 3018
# Plot unemploy as a function of date using a line plot
ggplot(economics, aes(x = date, y = unemploy)) + geom_line()
# Adjust plot to represent the fraction of total population that is unemployed
ggplot(economics, aes(x = date, y = unemploy/pop)) + geom_line()
recess <- data.frame(begin=as.Date(c(-31, 1400, 3652, 4199, 7486, 11382), origin="1970-01-01"), end=as.Date(c(304, 1885, 3834, 4687, 7729, 11627), origin="1970-01-01"))
ggplot(economics, aes(x = date, y = unemploy/pop)) +
geom_line() +
geom_rect(data=recess, inherit.aes=FALSE, aes(xmin=begin, xmax=end, ymin=-Inf, ymax=+Inf), fill="red", alpha=0.2)
# Cannot find dataset . . .
# Check the structure as a starting point
# str(fish.species)
# Use gather to go from fish.species to fish.tidy
# fish.tidy <- gather(fish.species, Species, Capture, -Year)
# Recreate the plot shown on the right
# ggplot(fish.tidy, aes(x = Year, y = Capture, col=Species)) + geom_line()
The qplot functionality is for making quick and dirty plots:
Basically, the qplot() is nice for just a quick and dirty analysis, though it will have much less flexibility on a go-forward basis.
Example code for qplot includes:
# The old way (shown)
plot(mpg ~ wt, data = mtcars)
# Using ggplot:
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()
# Using qplot:
qplot(wt, mpg, data=mtcars)
# Categorical:
# cyl
qplot(wt, mpg, data=mtcars, size=cyl)
## Warning: Using size for a discrete variable is not advised.
# gear
qplot(wt, mpg, data=mtcars, size=gear)
# Continuous
# hp
qplot(wt, mpg, data=mtcars, col=hp)
# qsec
qplot(wt, mpg, data=mtcars, col=qsec)
# qplot() with x only
qplot(factor(cyl), data=mtcars)
# qplot() with x and y
qplot(factor(cyl), factor(vs), data=mtcars)
# qplot() with geom set to jitter manually
qplot(factor(cyl), factor(vs), data=mtcars, geom="jitter")
# Make a dot plot with ggplot
ggplot(mtcars, aes(x=cyl, y=wt, fill = factor(am))) +
geom_dotplot(stackdir="center", binaxis="y")
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
# qplot with geom "dotplot", binaxis = "y" and stackdir = "center"
qplot(cyl, wt, fill=factor(am), data=mtcars, geom="dotplot", binaxis="y", stackdir="center")
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
Course #1 wrap-up comments:
A few warp-up coding exercises for ggplot #1 include:
# Check out the head of ChickWeight
data(ChickWeight)
head(ChickWeight)
## weight Time Chick Diet
## 1 42 0 1 1
## 2 51 2 1 1
## 3 59 4 1 1
## 4 64 6 1 1
## 5 76 8 1 1
## 6 93 10 1 1
# Use ggplot() for the second instruction
ggplot(ChickWeight, aes(x=Time, y=weight)) + geom_line(aes(group=Chick))
# Use ggplot() for the third instruction
ggplot(ChickWeight, aes(x=Time, y=weight, col=Diet)) + geom_line(aes(group=Chick))
# Use ggplot() for the last instruction
ggplot(ChickWeight, aes(x=Time, y=weight, col=Diet)) + geom_line(aes(group=Chick), alpha=0.3) + geom_smooth(lwd=2, se=FALSE)
## `geom_smooth()` using method = 'loess'
# Check out the structure of titanic
library(titanic)
## Warning: package 'titanic' was built under R version 3.2.5
library(dplyr)
titanicFull <- titanic::titanic_train
str(titanicFull)
## 'data.frame': 891 obs. of 12 variables:
## $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
## $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr "" "C85" "" "C123" ...
## $ Embarked : chr "S" "C" "S" "S" ...
titanic <- titanicFull %>%
select(Pclass, Sex, Survived, Age) %>%
filter(complete.cases(.))
str(titanic)
## 'data.frame': 714 obs. of 4 variables:
## $ Pclass : int 3 1 3 1 3 1 3 3 2 3 ...
## $ Sex : chr "male" "female" "female" "female" ...
## $ Survived: int 0 1 1 1 0 0 0 1 1 1 ...
## $ Age : num 22 38 26 35 35 54 2 27 14 4 ...
# Use ggplot() for the first instruction
ggplot(titanic, aes(x=factor(Pclass), fill=factor(Sex))) +
geom_bar(position = "dodge")
# Use ggplot() for the second instruction
ggplot(titanic, aes(x=factor(Pclass), fill=factor(Sex))) +
geom_bar(position = "dodge") +
facet_grid(. ~ Survived)
# Position jitter (use below)
posn.j <- position_jitter(0.5, 0)
# Use ggplot() for the last instruction
ggplot(titanic, aes(x=factor(Pclass), y=Age, col=factor(Sex))) +
geom_jitter(size=3, alpha=0.5, position=posn.j) +
facet_grid(. ~ Survived)
The second course expands on the remaining layers of ggplot2: Statistics, Coordinates, Facets, and Themes.
The statistics layer for ggplot2 has two basic components:
The statistics can also be called independently (outside the geom):
Example code from mtcars includes:
# Explore the mtcars data frame with str()
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# A scatter plot with LOESS smooth:
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth()
## `geom_smooth()` using method = 'loess'
# A scatter plot with an ordinary Least Squares linear model:
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm")
# The previous plot, without CI ribbon:
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se=FALSE)
# The previous plot, without points:
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_smooth(method = "lm", se=FALSE)
# Define cyl as a factor variable
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
geom_point() +
stat_smooth(method = "lm", se = FALSE)
# Complete the following ggplot command as instructed
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
geom_point() +
stat_smooth(method = "lm", se = FALSE) +
stat_smooth(method = "lm", se = FALSE, aes(group=1))
# Plot 1: change the LOESS span
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
# Add span below
geom_smooth(se = FALSE, span=0.7)
## `geom_smooth()` using method = 'loess'
# Plot 2: Set the overall model to LOESS and use a span of 0.7
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
geom_point() +
stat_smooth(method = "lm", se = FALSE) +
# Change method and add span below
stat_smooth(method = "loess", aes(group = 1),
se = FALSE, col = "black", span=0.7)
# Plot 3: Set col to "All", inside the aes layer of stat_smooth()
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
geom_point() +
stat_smooth(method = "lm", se = FALSE) +
stat_smooth(method = "loess",
# Add col inside aes()
aes(group = 1, col="All"),
# Remove the col argument below
se = FALSE, span = 0.7)
# Plot 4: Add scale_color_manual to change the colors
myColors <- c(brewer.pal(3, "Dark2"), "black")
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
geom_point() +
stat_smooth(method = "lm", se = FALSE, span = 0.75) +
stat_smooth(method = "loess",
aes(group = 1, col="All"),
se = F, span = 0.7) +
# Add correct arguments to scale_color_manual
scale_color_manual("Cylinders", values=myColors)
# Display structure of mtcars
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# Convert cyl and am to factors:
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$am <- as.factor(mtcars$am)
# Define positions:
posn.d <- position_dodge(width = 0.1)
posn.jd <- position_jitterdodge(jitter.width = 0.1, dodge.width = 0.2)
posn.j <- position_jitter(width = 0.2)
# base layers:
wt.cyl.am <- ggplot(mtcars, aes(x=cyl, y=wt, col=am, fill=am, group=am))
# Plot 1: Jittered, dodged scatter plot with transparent points
wt.cyl.am +
geom_point(position = posn.jd, alpha = 0.6)
# Plot 2: Mean and SD - the easy way
wt.cyl.am +
geom_point(position = posn.jd, alpha = 0.6) +
stat_summary(fun.data=mean_sdl, fun.args=list(mult=1), position=posn.d)
# Plot 3: Mean and 95% CI - the easy way
wt.cyl.am +
geom_point(position = posn.jd, alpha = 0.6) +
stat_summary(fun.data=mean_cl_normal, position=posn.d)
# Plot 4: Mean and SD - with T-tipped error bars - fill in ___
wt.cyl.am +
stat_summary(geom = "point", fun.y = mean,
position = posn.d) +
stat_summary(geom = "errorbar", fun.data = mean_sdl,
position = posn.d, fun.args = list(mult = 1), width = 0.1)
xx <- 1:100
# Function to save range for use in ggplot
gg_range <- function(x) {
# Change x below to return the instructed values
data.frame(ymin = min(x), # Min
ymax = max(x)
) # Max
}
gg_range(xx)
## ymin ymax
## 1 1 100
# Required output:
# ymin ymax
# 1 1 100
# Function to Custom function:
med_IQR <- function(x) {
# Change x below to return the instructed values
data.frame(y = median(x), # Median
ymin = quantile(x, 0.25), # 1st quartile
ymax = quantile(x, 0.75)
) # 3rd quartile
}
med_IQR(xx)
## y ymin ymax
## 25% 50.5 25.75 75.25
# Required output:
# y ymin ymax
# 25% 50.5 25.75 75.25
wt.cyl.am <- ggplot(mtcars, aes(x = cyl,y = wt, col = am, fill = am, group = am))
# Add three stat_summary calls to wt.cyl.am
wt.cyl.am +
stat_summary(geom = "linerange", fun.data = med_IQR,
position = posn.d, size = 3) +
stat_summary(geom = "linerange", fun.data = gg_range,
position = posn.d, size = 3,
alpha = 0.4) +
stat_summary(geom = "point", fun.y = median,
position = posn.d, size = 3,
col = "black", shape = "X")
Further examples (cached) from car::Vocab include:
Vocab <- car::Vocab
# Plot 1: Jittered scatter plot, add a linear model (lm) smooth:
ggplot(Vocab, aes(x = education, y = vocabulary)) +
geom_jitter(alpha = 0.2) +
stat_smooth(method="lm", se=FALSE)
# Plot 2: Only lm, colored by year
ggplot(Vocab, aes(x = education, y = vocabulary, col=factor(year))) +
stat_smooth(method="lm", se=FALSE)
# Plot 3: Set a color brewer palette
ggplot(Vocab, aes(x = education, y = vocabulary, col=factor(year))) +
stat_smooth(method="lm", se=FALSE) +
scale_color_brewer()
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colors
# Plot 4: Add the group, specify alpha and size
ggplot(Vocab, aes(x = education, y = vocabulary, col = year, group=factor(year))) +
stat_smooth(method = "lm", se = FALSE, alpha=0.6, size=2) +
scale_color_gradientn(colors = brewer.pal(9,"YlOrRd"))
# Use stat_quantile instead of stat_smooth:
ggplot(Vocab, aes(x = education, y = vocabulary, col = year, group = factor(year))) +
stat_quantile(alpha = 0.6, size = 2) +
scale_color_gradientn(colors = brewer.pal(9,"YlOrRd"))
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique
# Set quantile to 0.5:
ggplot(Vocab, aes(x = education, y = vocabulary, col = year, group = factor(year))) +
stat_quantile(alpha = 0.6, size = 2, quantiles=c(0.5)) +
scale_color_gradientn(colors = brewer.pal(9,"YlOrRd"))
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique
# Plot with linear and loess model
p <- ggplot(Vocab, aes(x = education, y = vocabulary)) +
stat_smooth(method = "loess", aes(col = "red"), se = F) +
stat_smooth(method = "lm", aes(col = "blue"), se = F) +
scale_color_discrete("Model", labels = c("red" = "LOESS", "blue" = "lm"))
# Add stat_sum
p + stat_sum()
# Add stat_sum and set size range
p + stat_sum() + scale_size(range = c(1, 10))
The coordinates layers control the plot dimensions:
The facets are based on the concept of small multiples as per the Tufte book on “Visulaization of Quantitative Information” (1983):
Example code includes:
data(mtcars);
mtcars$cyl <- as.factor(mtcars$cyl);
mtcars$am <- as.factor(mtcars$am)
# Basic ggplot() command, coded for you
p <- ggplot(mtcars, aes(x = wt, y = hp, col = am)) + geom_point() + geom_smooth()
# Add scale_x_continuous
p + scale_x_continuous(limits = c(3,6), expand=c(0,0))
## `geom_smooth()` using method = 'loess'
## Warning: Removed 12 rows containing non-finite values (stat_smooth).
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 3.168
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 4e-006
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 3.168
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.002
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 3.572
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 4e-006
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4e-006
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger
## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)
## Warning: Removed 12 rows containing missing values (geom_point).
# The proper way to zoom in:
p + coord_cartesian(xlim=c(3, 6))
## `geom_smooth()` using method = 'loess'
data(iris)
# Complete basic scatter plot function
base.plot <- ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, col=Species)) +
geom_jitter() +
geom_smooth(method = "lm", se = FALSE)
# Plot base.plot: default aspect ratio
base.plot
# Fix aspect ratio (1:1) of base.plot
base.plot + coord_equal()
# Create stacked bar plot: thin.bar
thin.bar <- ggplot(mtcars, aes(x=1, fill=cyl)) +
geom_bar()
# Convert thin.bar to pie chart
thin.bar + coord_polar(theta = "y")
# Create stacked bar plot: wide.bar
wide.bar <- ggplot(mtcars, aes(x=1, fill=cyl)) +
geom_bar(width=1)
# Convert wide.bar to pie chart
wide.bar + coord_polar(theta="y")
# Basic scatter plot:
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point()
# Separate rows according to transmission type, am
p + facet_grid(am ~ .)
# Separate columns according to cylinders, cyl
p + facet_grid(. ~ cyl)
# Separate by both columns and rows
p + facet_grid(am ~ cyl)
# Code to create the cyl_am col and myCol vector
mtcars$cyl_am <- paste(mtcars$cyl, mtcars$am, sep = "_")
myCol <- rbind(brewer.pal(9, "Blues")[c(3,6,8)],
brewer.pal(9, "Reds")[c(3,6,8)])
# Basic scatter plot, add color scale:
ggplot(mtcars, aes(x = wt, y = mpg, col=cyl_am)) +
geom_point() + scale_color_manual(values = myCol)
# Facet according on rows and columns.
ggplot(mtcars, aes(x = wt, y = mpg, col=cyl_am)) +
geom_point() + scale_color_manual(values = myCol) +
facet_grid(gear ~ vs)
# Add more variables
ggplot(mtcars, aes(x = wt, y = mpg, col=cyl_am, size=disp)) +
geom_point() + scale_color_manual(values = myCol) +
facet_grid(gear ~ vs)
mamsleep <- tidyr::gather(ggplot2::msleep %>%
mutate(total = sleep_total, rem=sleep_rem) %>%
select(vore, name, total, rem) %>%
filter(!is.na(total), !is.na(rem)),
sleep, time, -c(vore, name))
mamsleep$sleep <- factor(mamsleep$sleep, levels=c("total", "rem"))
str(mamsleep)
## Classes 'tbl_df', 'tbl' and 'data.frame': 122 obs. of 4 variables:
## $ vore : chr "omni" "herbi" "omni" "herbi" ...
## $ name : chr "Owl monkey" "Mountain beaver" "Greater short-tailed shrew" "Cow" ...
## $ sleep: Factor w/ 2 levels "total","rem": 1 1 1 1 1 1 1 1 1 1 ...
## $ time : num 17 14.4 14.9 4 14.4 8.7 10.1 5.3 9.4 10 ...
# Basic scatter plot
ggplot(mamsleep, aes(x=time, y=name, col=sleep)) + geom_point()
# Facet rows accoding to vore
ggplot(mamsleep, aes(x=time, y=name, col=sleep)) + geom_point() + facet_grid(vore ~ .)
# Specify scale and space arguments to free up rows
ggplot(mamsleep, aes(x=time, y=name, col=sleep)) + geom_point() +
facet_grid(vore ~ ., scale="free_y", space="free_y")
The themes layer controls all the “non-data ink” on your plot:
Often an objective to repeat the theme multiple times in the same presentation:
Example code includes:
# Rough re-engineer of what is pre-defined as z
data(mtcars)
mtcars$cyl <- factor(mtcars$cyl)
myColors <- c(brewer.pal(9, "Blues"))[c(5, 7, 9)]
origZ <- ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) +
geom_point(size=2) +
geom_smooth(method="lm", se=FALSE) +
facet_wrap(~ cyl, nrow=1) +
scale_x_continuous("Weight (lb/1000)") +
scale_y_continuous("Miles / (US) gallon") +
scale_color_manual("Cylinders", values=myColors)
z <- origZ
# Plot 1: change the plot background color to myPink:
myPink <- "#FEE0D2"
z + theme(plot.background = element_rect(fill = myPink))
# Plot 2: adjust the border to be a black line of size 3
z + theme(plot.background = element_rect(fill = myPink, color="black", size=3))
# Plot 3: set panel.background, legend.key, legend.background and strip.background to element_blank()
uniform_panels <- theme(panel.background = element_blank(),
legend.key = element_blank(),
legend.background=element_blank(),
strip.background = element_blank())
z <- z + theme(plot.background = element_rect(fill = myPink, color="black", size=3)) + uniform_panels
z
# Extend z with theme() function and three arguments
z <- z + theme(panel.grid=element_blank(), axis.line=element_line(color="black"),
axis.ticks=element_line(color="black")
)
z
# Extend z with theme() function and four arguments
myRed <- "#99000D"
z <- z + theme(strip.text = element_text(size=16, color=myRed),
axis.title.y=element_text(color=myRed, hjust=0, face="italic"),
axis.title.x=element_text(color=myRed, hjust=0, face="italic"),
axis.text=element_text(color="black")
)
z
# Move legend by position
z + theme(legend.position = c(0.85, 0.85))
# Change direction
z + theme(legend.direction = "horizontal")
# Change location by name
z + theme(legend.position = "bottom")
# Remove legend entirely
z + theme(legend.position = "none")
# Increase spacing between facets
library(grid)
z + theme(panel.margin.x = unit(2, "cm"))
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin.x` is deprecated. Please use `panel.spacing.x`
## property instead
# Add code to remove any excess plot margin space
z + theme(panel.margin.x = unit(2, "cm"), plot.margin = unit(c(0,0,0,0), "cm"))
## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead
## Warning: `panel.margin.x` is deprecated. Please use `panel.spacing.x`
## property instead
# Make z2 the same as origZ
z2 <- origZ
# Theme layer saved as an object, theme_pink
theme_pink <- theme(panel.background = element_blank(),
legend.key = element_blank(),
legend.background = element_blank(),
strip.background = element_blank(),
plot.background = element_rect(fill = myPink, color = "black", size = 3),
panel.grid = element_blank(),
axis.line = element_line(color = "black"),
axis.ticks = element_line(color = "black"),
strip.text = element_text(size = 16, color = myRed),
axis.title.y = element_text(color = myRed, hjust = 0, face = "italic"),
axis.title.x = element_text(color = myRed, hjust = 0, face = "italic"),
axis.text = element_text(color = "black"),
legend.position = "none")
# Apply theme_pink to z2
z2 + theme_pink
# Change code so that old theme is saved as old
old <- theme_update(panel.background = element_blank(),
legend.key = element_blank(),
legend.background = element_blank(),
strip.background = element_blank(),
plot.background = element_rect(fill = myPink, color = "black", size = 3),
panel.grid = element_blank(),
axis.line = element_line(color = "black"),
axis.ticks = element_line(color = "black"),
strip.text = element_text(size = 16, color = myRed),
axis.title.y = element_text(color = myRed, hjust = 0, face = "italic"),
axis.title.x = element_text(color = myRed, hjust = 0, face = "italic"),
axis.text = element_text(color = "black"),
legend.position = "none")
# Display the plot z2
z2
# Restore the old plot
theme_set(old)
# Load ggthemes package
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 3.2.5
# Apply theme_tufte
z2 + theme_tufte()
# Apply theme_tufte, modified:
z2 + theme_tufte() +
theme(legend.position = c(0.9, 0.9), legend.title=element_text(face="italic", size=12), axis.title=element_text(face="bold", size=14))
Best practices for plotting and visulaization:
Recall that pie charts are just bar charts wrapped on to polar coordinates:
Suggestion that heat maps are “one of the least effective forms of communication”:
Example code includes:
data(mtcars)
mtcars$cyl <- factor(mtcars$cyl)
mtcars$am <- factor(mtcars$am)
m <- ggplot(mtcars, aes(x = cyl, y = wt))
# Draw dynamite plot
m +
stat_summary(fun.y = mean, geom = "bar", fill = "skyblue") +
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar", width = 0.1)
# Base layers
m <- ggplot(mtcars, aes(x = cyl, y = wt, col = am, fill = am))
# Plot 1: Draw dynamite plot
m +
stat_summary(fun.y = mean, geom = "bar") +
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar", width = 0.1)
# Plot 2: Set position dodge in each stat function
m +
stat_summary(fun.y = mean, geom = "bar", position = "dodge") +
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1),
geom = "errorbar", width = 0.1, position = "dodge")
# Set your dodge posn manually
posn.d <- position_dodge(0.9)
# Plot 3: Redraw dynamite plot
m +
stat_summary(fun.y = mean, geom = "bar", position = posn.d) +
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar",
width = 0.1, position = posn.d
)
cyl_means <- tapply(mtcars$wt, mtcars$cyl, FUN=mean)
cyl_sd <- tapply(mtcars$wt, mtcars$cyl, FUN=sd)
cyl_ct <- tapply(mtcars$wt, mtcars$cyl, FUN=length)
mtcars.cyl <- data.frame(cyl=as.factor(names(cyl_means)),
wt.avg=cyl_means, sd=cyl_sd, prop=cyl_ct/sum(cyl_ct)
)
# Base layers
m <- ggplot(mtcars.cyl, aes(x = cyl, y = wt.avg))
# Plot 1: Draw bar plot
m + geom_bar(stat = "identity", fill="skyblue")
# Plot 2: Add width aesthetic
m + geom_bar(stat = "identity", fill="skyblue", aes(width=prop))
## Warning: Ignoring unknown aesthetics: width
# Plot 3: Add error bars
m + geom_bar(stat = "identity", fill="skyblue", aes(width=prop)) +
geom_errorbar(aes(ymin = wt.avg - sd, ymax = wt.avg + sd), width=0.1)
## Warning: Ignoring unknown aesthetics: width
ggplot(mtcars, aes(x = factor(1), fill = am)) +
geom_bar(position = "fill", width=1) +
facet_grid(. ~ cyl) +
coord_polar(theta="y")
library(GGally)
## Warning: package 'GGally' was built under R version 3.2.5
## Warning: replacing previous import by 'utils::capture.output' when loading
## 'GGally'
## Warning: replacing previous import by 'utils::head' when loading 'GGally'
## Warning: replacing previous import by 'utils::installed.packages' when
## loading 'GGally'
## Warning: replacing previous import by 'utils::str' when loading 'GGally'
##
## Attaching package: 'GGally'
## The following object is masked from 'package:dplyr':
##
## nasa
# All columns except am
group_by_am <- match("am", names(mtcars))
my_names_am <- (1:11)[-group_by_am]
# Basic parallel plot - each variable plotted as a z-score transformation
ggparcoord(mtcars, my_names_am, groupColumn = group_by_am, alpha = 0.8)
barley <- lattice::barley
str(barley)
## 'data.frame': 120 obs. of 4 variables:
## $ yield : num 27 48.9 27.4 39.9 33 ...
## $ variety: Factor w/ 10 levels "Svansota","No. 462",..: 3 3 3 3 3 3 7 7 7 7 ...
## $ year : Factor w/ 2 levels "1932","1931": 2 2 2 2 2 2 2 2 2 2 ...
## $ site : Factor w/ 6 levels "Grand Rapids",..: 3 6 4 5 1 2 3 6 4 5 ...
# Create color palette
myColors <- brewer.pal(9, "Reds")
# Build the heat map from scratch
ggplot(barley, aes(x=year, y=variety, fill=yield)) +
geom_tile() +
facet_wrap(~ site, ncol=1) +
scale_fill_gradientn(colors = myColors)
# Line plots
ggplot(barley, aes(x=year, y=yield, col=variety, group=variety)) +
geom_line() +
facet_wrap(~ site, nrow=1)
ggplot(barley, aes(x=year, y=yield, col=site, group=site, fill=site)) +
stat_summary(fun.y = mean, geom="line") +
stat_summary(fun.data = mean_sdl, fun.args=list(mult=1), geom="ribbon", col=NA, alpha=0.1)
CHIS (California Health Information Study) - Descriptive Statistics Case Study:
The mosaic plot is a sequence of rectangles, each sized based on the size of the group:
Case study code - creating a Merimeko plot:
# TBD - need the raw data first!
Advanced course that builds on concepts from the previous modules:
Refresher code from previous modules includes:
# Create movies_small
library(ggplot2movies)
## Warning: package 'ggplot2movies' was built under R version 3.2.5
set.seed(123)
movies_small <- movies[sample(nrow(movies), 1000), ]
movies_small$rating <- factor(round(movies_small$rating))
# Create movies_small
library(ggplot2movies)
set.seed(123)
movies_small <- movies[sample(nrow(movies), 1000), ]
movies_small$rating <- factor(round(movies_small$rating))
# Explore movies_small with str()
str(movies_small)
## Classes 'tbl_df', 'tbl' and 'data.frame': 1000 obs. of 24 variables:
## $ title : chr "Fair and Worm-er" "Shelf Life" "House: After Five Years of Living" "Three Long Years" ...
## $ year : int 1946 2000 1955 2003 1963 1992 1999 1972 1994 1985 ...
## $ length : int 7 4 11 76 103 107 87 84 127 94 ...
## $ budget : int NA NA NA NA NA NA NA NA NA NA ...
## $ rating : Factor w/ 10 levels "1","2","3","4",..: 7 7 6 8 8 5 4 8 5 5 ...
## $ votes : int 16 11 15 11 103 28 105 9 37 28 ...
## $ r1 : num 0 0 14.5 4.5 4.5 4.5 14.5 0 4.5 4.5 ...
## $ r2 : num 0 0 0 0 4.5 0 4.5 0 4.5 0 ...
## $ r3 : num 0 0 4.5 4.5 0 4.5 4.5 0 14.5 4.5 ...
## $ r4 : num 0 0 4.5 0 4.5 4.5 4.5 0 4.5 14.5 ...
## $ r5 : num 4.5 4.5 0 0 4.5 0 4.5 14.5 24.5 4.5 ...
## $ r6 : num 4.5 24.5 34.5 4.5 4.5 0 14.5 0 4.5 14.5 ...
## $ r7 : num 64.5 4.5 24.5 0 14.5 4.5 14.5 14.5 14.5 14.5 ...
## $ r8 : num 14.5 24.5 4.5 4.5 14.5 24.5 14.5 24.5 14.5 14.5 ...
## $ r9 : num 0 0 0 14.5 14.5 24.5 14.5 14.5 4.5 4.5 ...
## $ r10 : num 14.5 24.5 14.5 44.5 44.5 24.5 14.5 44.5 4.5 24.5 ...
## $ mpaa : chr "" "" "" "" ...
## $ Action : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Animation : int 1 0 0 0 0 0 0 0 0 0 ...
## $ Comedy : int 1 0 0 1 0 1 1 1 0 0 ...
## $ Drama : int 0 0 0 0 1 0 0 0 1 1 ...
## $ Documentary: int 0 0 1 0 0 0 0 0 0 0 ...
## $ Romance : int 0 0 0 0 0 0 1 0 0 0 ...
## $ Short : int 1 1 1 0 0 0 0 0 0 0 ...
# Build a scatter plot with mean and 95% CI
ggplot(movies_small, aes(x = rating, y = votes)) +
geom_point() +
stat_summary(fun.data = "mean_cl_normal",
geom = "crossbar",
width = 0.2,
col = "red") +
scale_y_log10()
# Reproduce the plot
ggplot(diamonds, aes(x=carat, y=price, col=color)) +
geom_point(alpha=0.5, size=0.5, shape=16) +
scale_x_log10(limits=c(0.1, 10)) +
xlab(expression(log[10](Carat))) +
scale_y_log10(limits=c(100, 100000)) +
ylab(expression(log[10](Price))) +
scale_color_brewer(palette="YlOrRd") +
coord_equal() +
theme_classic()
# Add smooth layer and facet the plot
ggplot(diamonds, aes(x = carat, y = price, col = color)) +
geom_point(alpha = 0.5, size = .5, shape = 16) +
scale_x_log10(expression(log[10](Carat)), limits = c(0.1,10)) +
scale_y_log10(expression(log[10](Price)), limits = c(100,100000)) +
scale_color_brewer(palette = "YlOrRd") +
coord_equal() +
theme_classic() +
stat_smooth(method="lm") +
facet_grid(. ~ cut)
Box plots creation and usage (more for an academic audience). John Tukey - “Exploratory Data Analysis” (median, IQR, max, min, extremes outside of 1.5 * IQR). Whiskers are only drawn up to the “fence” (1.5 * IQR); everything above this is the outlier.
Example code includes:
# Add a boxplot geom
d <- ggplot(movies_small, aes(x = rating, y = votes)) +
geom_point() +
geom_boxplot() +
stat_summary(fun.data = "mean_cl_normal",
geom = "crossbar",
width = 0.2,
col = "red")
# Untransformed plot
d
# Transform the scale
d + scale_y_log10()
# Transform the coordinates (produces error in RStudio - commented out)
# d + coord_trans(y = "log10")
# Plot object p
p <- ggplot(diamonds, aes(x = carat, y = price))
# Use cut_interval
p + geom_boxplot(aes(group = cut_interval(carat, n=10)))
# Use cut_number
p + geom_boxplot(aes(group = cut_number(carat, n=10)))
# Use cut_width
p + geom_boxplot(aes(group = cut_width(carat, width=0.25)))
Density plots are a good way to describe univariate data. There are many statistics (e.g., PDF or Probability Density Function), including the “Kernel Density Estimate” (KDE) - “sum of bumps placed at the observations; kernel function determines the shape of the bumps, while window width, h, determines their width”:
Example code includes:
# Set up keys
t_norm <- c(-0.560475646552213,-0.23017748948328,1.55870831414912,0.070508391424576,0.129287735160946,1.71506498688328,0.460916205989202,-1.26506123460653,-0.686852851893526,-0.445661970099958,1.22408179743946,0.359813827057364,0.400771450594052,0.11068271594512,-0.555841134754075,1.78691313680308,0.497850478229239,-1.96661715662964,0.701355901563686,-0.472791407727934,-1.06782370598685,-0.217974914658295,-1.02600444830724,-0.72889122929114,-0.625039267849257,-1.68669331074241,0.837787044494525,0.153373117836515,-1.13813693701195,1.25381492106993,0.426464221476814,-0.295071482992271,0.895125661045022,0.878133487533042,0.821581081637487,0.688640254100091,0.553917653537589,-0.0619117105767217,-0.305962663739917,-0.380471001012383,-0.694706978920513,-0.207917278019599,-1.26539635156826,2.16895596533851,1.20796199830499,-1.12310858320335,-0.402884835299076,-0.466655353623219,0.779965118336318,-0.0833690664718293,0.253318513994755,-0.028546755348703,-0.0428704572913161,1.36860228401446,-0.225770985659268,1.51647060442954,-1.54875280423022,0.584613749636069,0.123854243844614,0.215941568743973,0.379639482759882,-0.502323453109302,-0.33320738366942,-1.01857538310709,-1.07179122647558,0.303528641404258,0.448209778629426,0.0530042267305041,0.922267467879738,2.05008468562714,-0.491031166056535,-2.30916887564081,1.00573852446226,-0.709200762582393,-0.688008616467358,1.0255713696967,-0.284773007051009,-1.22071771225454,0.18130347974915,-0.138891362439045,0.00576418589988693,0.38528040112633,-0.370660031792409,0.644376548518833,-0.220486561818751,0.331781963915697,1.09683901314935,0.435181490833803,-0.325931585531227,1.14880761845109,0.993503855962119,0.54839695950807,0.238731735111441,-0.627906076039371,1.36065244853001,-0.600259587147127,2.18733299301658,1.53261062618519,-0.235700359100477,-1.02642090030678,-0.710406563699301,0.25688370915653,-0.246691878462374,-0.347542599397733,-0.951618567265016,-0.0450277248089203,-0.784904469457076,-1.66794193658814,-0.380226520287762,0.918996609060766,-0.575346962608392,0.607964322225033,-1.61788270828916,-0.0555619655245394,0.519407203943462,0.301153362166714,0.105676194148943,-0.640706008305376,-0.849704346033582,-1.02412879060491,0.117646597100126,-0.947474614184802,-0.490557443700668,-0.256092192198247,1.84386200523221,-0.651949901695459,0.235386572284857,0.0779608495637108,-0.961856634130129,-0.0713080861235987,1.44455085842335,0.451504053079215,0.0412329219929399,-0.422496832339625,-2.05324722154052,1.13133721341418,-1.46064007092482,0.739947510877334,1.90910356921748,-1.4438931609718,0.701784335374711,-0.262197489402468,-1.57214415914549,-1.51466765378175,-1.60153617357459,-0.530906522170303,-1.4617555849959,0.687916772975828,2.10010894052567,-1.28703047603518,0.787738847475178,0.76904224100091,0.332202578950118,-1.00837660827701,-0.119452606630659,-0.280395335170247,0.56298953322048,-0.372438756103829,0.976973386685621,-0.374580857767014,1.05271146557933,-1.04917700666607,-1.26015524475811,3.2410399349424,-0.416857588160432,0.298227591540715,0.636569674033849,-0.483780625708744,0.516862044313609,0.368964527385086,-0.215380507641693,0.0652930335253153,-0.034067253738464,2.12845189901618,-0.741336096272828,-1.09599626707466,0.0377883991710788,0.310480749443137,0.436523478910183,-0.458365332711106,-1.06332613397119,1.26318517608949,-0.349650387953555,-0.865512862653374,-0.236279568941097,-0.197175894348552,1.10992028971364,0.0847372921971965,0.754053785184521,-0.499292017172261,0.214445309581601,-0.324685911490835,0.0945835281735714,-0.895363357977542,-1.31080153332797,1.99721338474797,0.600708823672418,-1.25127136162494,-0.611165916680421,-1.18548008459731)
t_bimodal <- c(0.19881034888372,-0.68758702356649,-2.26514505669635,-1.45680594076791,-2.41433994791886,-2.47624689461558,-2.78860283785024,-2.59461726745951,-0.34909253266331,-2.05402812508544,-1.88075476357242,-1.75631257040091,-0.767524121514662,-2.51606383094478,-2.99250715039204,-0.32430306759681,-2.44116321690529,-2.72306596993987,-3.23627311888329,-3.2847157223178,-2.57397347929799,-1.38201418283347,-0.890151861070282,-1.29241164616441,-2.36365729709525,-1.9402500626154,-2.70459646368007,-2.71721816157401,-1.11534950102308,-3.01559257860354,-0.0447060345075361,-2.09031959396585,-1.78546117337078,-2.73852770473957,-2.57438868976327,-3.31701613230524,-2.18292538837273,-1.58101759507554,-1.67569565583862,-2.78153648705475,-2.788621970854,-2.50219871834286,-0.503939330153649,-3.13730362066574,-2.1790515943802,-0.0976381783210729,-2.10097488532881,-3.35984070382139,-2.66476943527406,-1.51454002109512,-2.37560287166977,-2.56187636354978,-2.34391723412846,-1.90950335286078,-0.401491228854174,-2.08856511213888,-0.919200503848483,-1.36924588434943,-2.11363989550614,-3.5329020028906,-2.52111731755252,-2.48987045313847,-1.95284556723847,-0.699801322333179,0.293078973831094,-0.452418941016231,-2.13315096432894,-3.75652739555764,-2.38877986407174,-1.91079277692671,-1.15498699593256,-1.03747203151573,-1.31569057058354,-3.39527434979947,-1.15035695436664,-2.44655721642722,-1.82519729983874,-1.92544882282627,-1.57183323502949,-1.97532501717386,-3.66747509758566,-1.26350403522656,-1.61397343165032,-2.26565162527822,-1.88185548895332,-1.86596135463154,-1.778980531439,-0.359153834022514,-2.21905037893348,-1.83193461611534,-0.831616126930907,-0.945818976623081,-0.854736889619643,-2.57746800105956,0.00248273029282942,-1.93329912906982,-0.133148155293138,-3.35090268603071,-1.97901641364576,-0.750085429030784,1.2847578127772,1.24731103178226,1.06146129639311,0.947486720661263,1.5628404668196,2.33117917295898,-0.01421049792072,2.21198043337229,3.23667504641657,4.03757401824044,3.30117599220059,2.75677476379596,0.27326960088567,1.39849329199322,1.64795354341738,2.70352390275689,1.89432866599623,0.741351371939828,3.68443570809411,2.91139129179596,2.23743027249103,3.21810861032581,0.661225712765028,2.6608202977898,1.47708762368658,2.68374552185071,1.93917804533993,2.63296071303145,3.33551761505939,2.0072900903169,3.01755863695209,0.811565964852021,1.27839555956398,3.51921771138818,2.37738797302393,-0.0522228204337298,0.635962547917625,1.79921898441088,2.86577940433449,1.89811674428478,2.62418747202065,2.95900537778783,3.67105482886294,2.05601673327496,1.9480180938191,0.24676264085773,2.09932759408783,1.42814994210444,1.02599041719591,1.82009376895246,3.01494317274366,0.00725151131141755,1.57272071279457,2.11663728358271,1.10679242994505,2.33390294249923,2.41142992061573,1.96696384072401,-0.465898193760027,4.57145814586664,1.7947007425318,2.65119328158767,2.27376649103655,3.02467323481835,2.81765944637409,1.79020682877149,2.37816777220851,1.05459116887611,2.85692301089932,1.53896166111565,4.41677335378821,0.348951104311813,1.53601275703399,2.82537986275924,2.51013254687866,1.410518961485,1.00321925779248,2.1444757047107,1.98569258683309,0.20971876273594,2.03455106713385,2.19023031569246,2.17472639698184,0.944982957397319,2.47613327830263,3.37857013695924,2.45623640317981,0.864411529625657,1.5643545303081,2.34610361955361,1.35295436868173,-0.157646335015277,2.88425082002821,1.17052238837548,1.42643972923172,3.50390060900454,1.22585507039459,2.8457315401893,0.739317121181225,1.64545759692602)
t_uniform <- c(-0.117272665724158,-0.536618106998503,-1.51491178292781,-1.81202527787536,-0.948814783245325,1.87456467188895,-0.0460180705413222,-0.0887118810787797,0.995171524584293,0.670560925267637,-1.80233761295676,0.780420977622271,-0.546953733079135,1.53653442952782,1.10118891857564,-1.44318543467671,-0.819962915033102,-1.49566885828972,0.359606495127082,0.246702435426414,0.754884480498731,-0.754916763864458,0.422347365878522,1.96413727570325,0.972819660790265,-1.69657147862017,-0.195324374362826,-1.78585226181895,-0.641777943819761,0.935808595269918,-1.98357223812491,1.08763792924583,-0.148099155165255,0.883361400105059,0.666022868826985,0.288294882513583,0.815251800231636,0.628884241916239,-0.842591419816017,-1.61104217730463,1.84968528430909,0.945336116477847,0.450893874280155,-1.52028447668999,0.201036185957491,-0.948974888771772,1.5934433247894,-1.96328021679074,-1.05506025627255,-1.47982161864638,-0.695067413151264,0.90559555310756,1.96698094159365,0.86053411103785,0.0177592439576983,-0.255809312686324,1.79530100245029,-1.51927404943854,-1.69941929355264,1.55608597118407,-0.302191619761288,-1.8305572848767,0.5897685745731,-0.125523350201547,0.471704493276775,-0.916738269850612,-1.3708188533783,-1.54298291914165,0.0307314526289701,0.192129150032997,-1.43741524219513,-1.32092379964888,1.0479412926361,0.1095797624439,1.44395743031055,0.69421995151788,-1.94783588126302,0.77279560547322,1.56685492862016,0.52740070130676,-1.57082138024271,1.684260815382,0.701449524611235,-1.40562514960766,0.981367369182408,1.77039544750005,-0.316866497509181,-0.809107687324286,-0.962293319404125,-1.10847473237664,0.262617373839021,1.02660053130239,0.678414183668792,0.186091177165508,1.24585463106632,1.03666720818728,-1.9197261352092,-0.476385199464858,-1.79647954553366,1.19161174912006,1.69479685276747,0.170393481850624,1.40945840068161,0.33425145316869,0.673294574022293,0.0452583860605955,1.05100235715508,1.61344915162772,1.28189804870635,-1.71427260152996,-1.98441462777555,-1.79120553657413,1.46624071896076,0.304980675689876,-0.744629711844027,1.83786313515157,0.364775028079748,0.125637343153358,-0.464253313839436,-0.721787082031369,1.23354502115399,-1.83232200611383,-0.545029221102595,1.4263878678903,0.791786313988268,0.737945891916752,-0.607939789071679,0.218727317638695,-1.45102552976459,1.13972622528672,1.54745028633624,-1.18361646682024,1.08249184582382,0.385451843030751,1.83067896962166,-1.36524640955031,0.103897096589208,1.49260547012091,1.47882428113371,-1.90524542704225,1.90355877391994,-0.0391032071784139,-0.443318707868457,-0.329780160449445,-1.62829671800137,-1.35276315920055,-0.378334019333124,-0.632742223329842,-0.33897018712014,-0.783790123648942,0.241122149862349,-1.37650339771062,1.82631905656308,-1.82413349859416,-0.511369029060006,1.85046136565506,0.581710062921047,-1.75494954269379,-0.360216286033392,-0.296379463747144,0.0326323388144374,-0.201600356958807,0.493045534007251,-1.44008827582002,1.63178560324013,0.277773099020123,0.193122295662761,-1.53268950991333,1.04811332933605,-0.0865222131833434,1.12787565868348,-1.8158938055858,1.27937867119908,-0.922366210259497,-0.868598341941833,0.572886237874627,1.79247535113245,-1.97200618218631,-0.593529311940074,-0.323817100375891,-0.168492811731994,0.846770317293704,1.67939223069698,0.508442802354693,1.60872711334378,1.02931663673371,-1.44856653735042,-1.38698048796505,-1.2347512524575,-0.267259437590837,-1.65112279262394,-1.10487999580801,0.28859471809119,-0.399323286488652,0.261861522682011,1.31849551480263,0.568455277942121,-0.43400499317795,0.838319418951869,-1.56470370758325)
test_data <- data.frame(norm=t_norm, bimodal=t_bimodal, uniform=t_uniform)
small_data <- data.frame(x=c(-3.5, 0, 0.5, 6))
# Calculating density: d
d <- density(test_data$norm)
# Use which.max() to calculate mode
mode <- d$x[which.max(d$y)]
# Finish the ggplot call
ggplot(test_data, aes(x = norm)) +
geom_rug() +
geom_density() +
geom_vline(xintercept = mode, col = "red")
# Arguments you'll need later on
fun_args <- list(mean = mean(test_data$norm), sd = sd(test_data$norm))
# Finish the ggplot
ggplot(test_data, aes(x = norm)) +
geom_histogram(aes(y=..density..)) +
geom_density(col="red") +
stat_function(fun=dnorm, args=fun_args, col="blue")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Get the bandwith
get_bw <- density(small_data$x)$bw
# Basic plotting object
p <- ggplot(small_data, aes(x = x)) +
geom_rug() +
coord_cartesian(ylim = c(0,0.5))
# Create three plots
p + geom_density()
p + geom_density(adjust=0.25)
# p + geom_density(bw = 0.25*get_bw) ** does not work with my version of R/ggplot2 **
# Create two plots
p + geom_density(kernel="r")
p + geom_density(kernel="e")
There are multiple options for handling multiple groups (levels within a factor) or variables:
Example code includes:
# Create the data
norm0 <- c(-0.56, -0.23, 1.56, 0.07, 0.13, 1.72, 0.46, -1.27, -0.69, -0.45, 1.22, 0.36, 0.4, 0.11, -0.56, 1.79, 0.5, -1.97, 0.7, -0.47, -1.07, -0.22, -1.03, -0.73, -0.63, -1.69, 0.84, 0.15, -1.14, 1.25, 0.43, -0.3, 0.9, 0.88, 0.82, 0.69, 0.55, -0.06, -0.31, -0.38, -0.69, -0.21, -1.27, 2.17, 1.21, -1.12, -0.4, -0.47, 0.78, -0.08, 0.25, -0.03, -0.04, 1.37, -0.23, 1.52, -1.55, 0.58, 0.12, 0.22, 0.38, -0.5, -0.33, -1.02, -1.07, 0.3, 0.45, 0.05, 0.92, 2.05, -0.49, -2.31, 1.01, -0.71, -0.69, 1.03, -0.28, -1.22, 0.18, -0.14, 0.01, 0.39, -0.37, 0.64, -0.22, 0.33, 1.1, 0.44, -0.33, 1.15, 0.99, 0.55, 0.24, -0.63, 1.36, -0.6, 2.19, 1.53, -0.24, -1.03, -0.71, 0.26, -0.25, -0.35, -0.95, -0.05, -0.78, -1.67, -0.38, 0.92, -0.58, 0.61, -1.62, -0.06, 0.52, 0.3, 0.11, -0.64, -0.85, -1.02, 0.12, -0.95, -0.49, -0.26, 1.84, -0.65, 0.24, 0.08, -0.96, -0.07, 1.44, 0.45, 0.04, -0.42, -2.05, 1.13, -1.46, 0.74, 1.91, -1.44, 0.7, -0.26, -1.57, -1.51, -1.6, -0.53, -1.46, 0.69, 2.1, -1.29, 0.79, 0.77, 0.33, -1.01, -0.12, -0.28, 0.56, -0.37, 0.98, -0.37, 1.05, -1.05, -1.26, 3.24, -0.42, 0.3, 0.64, -0.48, 0.52, 0.37, -0.22, 0.07, -0.03, 2.13, -0.74, -1.1, 0.04, 0.31, 0.44, -0.46, -1.06, 1.26, -0.35, -0.87, -0.24, -0.2, 1.11, 0.08, 0.75, -0.5, 0.21, -0.32, 0.09, -0.9, -1.31, 2, 0.6, -1.25, -0.61, -1.19)
bimodal0 <- c(0.2, -0.69, -2.27, -1.46, -2.41, -2.48, -2.79, -2.59, -0.35, -2.05, -1.88, -1.76, -0.77, -2.52, -2.99, -0.32, -2.44, -2.72, -3.24, -3.28, -2.57, -1.38, -0.89, -1.29, -2.36, -1.94, -2.7, -2.72, -1.12, -3.02, -0.04, -2.09, -1.79, -2.74, -2.57, -3.32, -2.18, -1.58, -1.68, -2.78, -2.79, -2.5, -0.5, -3.14, -2.18, -0.1, -2.1, -3.36, -2.66, -1.51, -2.38, -2.56, -2.34, -1.91, -0.4, -2.09, -0.92, -1.37, -2.11, -3.53, -2.52, -2.49, -1.95, -0.7, 0.29, -0.45, -2.13, -3.76, -2.39, -1.91, -1.15, -1.04, -1.32, -3.4, -1.15, -2.45, -1.83, -1.93, -1.57, -1.98, -3.67, -1.26, -1.61, -2.27, -1.88, -1.87, -1.78, -0.36, -2.22, -1.83, -0.83, -0.95, -0.85, -2.58, 0, -1.93, -0.13, -3.35, -1.98, -0.75, 1.28, 1.25, 1.06, 0.95, 1.56, 2.33, -0.01, 2.21, 3.24, 4.04, 3.3, 2.76, 0.27, 1.4, 1.65, 2.7, 1.89, 0.74, 3.68, 2.91, 2.24, 3.22, 0.66, 2.66, 1.48, 2.68, 1.94, 2.63, 3.34, 2.01, 3.02, 0.81, 1.28, 3.52, 2.38, -0.05, 0.64, 1.8, 2.87, 1.9, 2.62, 2.96, 3.67, 2.06, 1.95, 0.25, 2.1, 1.43, 1.03, 1.82, 3.01, 0.01, 1.57, 2.12, 1.11, 2.33, 2.41, 1.97, -0.47, 4.57, 1.79, 2.65, 2.27, 3.02, 2.82, 1.79, 2.38, 1.05, 2.86, 1.54, 4.42, 0.35, 1.54, 2.83, 2.51, 1.41, 1, 2.14, 1.99, 0.21, 2.03, 2.19, 2.17, 0.94, 2.48, 3.38, 2.46, 0.86, 1.56, 2.35, 1.35, -0.16, 2.88, 1.17, 1.43, 3.5, 1.23, 2.85, 0.74, 1.65)
value2 <- c(-0.56, -0.23, 1.559, 0.071, 0.129, 1.715, 0.461, -1.265, -0.687, -0.446, 1.224, 0.36, 0.401, 0.111, -0.556, 1.787, 0.498, -1.967, 0.701, -0.473, -1.068, -0.218, -1.026, -0.729, -0.625, -1.687, 0.838, 0.153, -1.138, 1.254, 0.426, -0.295, 0.895, 0.878, 0.822, 0.689, 0.554, -0.062, -0.306, -0.38, -0.695, -0.208, -1.265, 2.169, 1.208, -1.123, -0.403, -0.467, 0.78, -0.083, 0.253, -0.029, -0.043, 1.369, -0.226, 1.516, -1.549, 0.585, 0.124, 0.216, 0.38, -0.502, -0.333, -1.019, -1.072, 0.304, 0.448, 0.053, 0.922, 2.05, -0.491, -2.309, 1.006, -0.709, -0.688, 1.026, -0.285, -1.221, 0.181, -0.139, 0.006, 0.385, -0.371, 0.644, -0.22, 0.332, 1.097, 0.435, -0.326, 1.149, 0.994, 0.548, 0.239, -0.628, 1.361, -0.6, 2.187, 1.533, -0.236, -1.026, -0.71, 0.257, -0.247, -0.348, -0.952, -0.045, -0.785, -1.668, -0.38, 0.919, -0.575, 0.608, -1.618, -0.056, 0.519, 0.301, 0.106, -0.641, -0.85, -1.024, 0.118, -0.947, -0.491, -0.256, 1.844, -0.652, 0.235, 0.078, -0.962, -0.071, 1.445, 0.452, 0.041, -0.422, -2.053, 1.131, -1.461, 0.74, 1.909, -1.444, 0.702, -0.262, -1.572, -1.515, -1.602, -0.531, -1.462, 0.688, 2.1, -1.287, 0.788, 0.769, 0.332, -1.008, -0.119, -0.28, 0.563, -0.372, 0.977, -0.375, 1.053, -1.049, -1.26, 3.241, -0.417, 0.298, 0.637, -0.484, 0.517, 0.369, -0.215, 0.065, -0.034, 2.128, -0.741, -1.096, 0.038, 0.31, 0.437, -0.458, -1.063, 1.263, -0.35, -0.866, -0.236, -0.197, 1.11, 0.085, 0.754, -0.499, 0.214, -0.325, 0.095, -0.895, -1.311, 1.997, 0.601, -1.251, -0.611, -1.185, 0.199, -0.688, -2.265, -1.457, -2.414, -2.476, -2.789, -2.595, -0.349, -2.054, -1.881, -1.756, -0.768, -2.516, -2.993, -0.324, -2.441, -2.723, -3.236, -3.285, -2.574, -1.382, -0.89, -1.292, -2.364, -1.94, -2.705, -2.717, -1.115, -3.016, -0.045, -2.09, -1.785, -2.739, -2.574, -3.317, -2.183, -1.581, -1.676, -2.782, -2.789, -2.502, -0.504, -3.137, -2.179, -0.098, -2.101, -3.36, -2.665, -1.515, -2.376, -2.562, -2.344, -1.91, -0.401, -2.089, -0.919, -1.369, -2.114, -3.533, -2.521, -2.49, -1.953, -0.7, 0.293, -0.452, -2.133, -3.757, -2.389, -1.911, -1.155, -1.037, -1.316, -3.395, -1.15, -2.447, -1.825, -1.925, -1.572, -1.975, -3.667, -1.264, -1.614, -2.266, -1.882, -1.866, -1.779, -0.359, -2.219, -1.832, -0.832, -0.946, -0.855, -2.577, 0.002, -1.933, -0.133, -3.351, -1.979, -0.75, 1.285, 1.247, 1.061, 0.947, 1.563, 2.331, -0.014, 2.212, 3.237, 4.038, 3.301, 2.757, 0.273, 1.398, 1.648, 2.704, 1.894, 0.741, 3.684, 2.911, 2.237, 3.218, 0.661, 2.661, 1.477, 2.684, 1.939, 2.633, 3.336, 2.007, 3.018, 0.812, 1.278, 3.519, 2.377, -0.052, 0.636, 1.799, 2.866, 1.898, 2.624, 2.959, 3.671, 2.056, 1.948, 0.247, 2.099, 1.428, 1.026, 1.82, 3.015, 0.007, 1.573, 2.117, 1.107, 2.334, 2.411, 1.967, -0.466, 4.571, 1.795, 2.651, 2.274, 3.025, 2.818, 1.79, 2.378, 1.055, 2.857, 1.539, 4.417, 0.349, 1.536, 2.825, 2.51, 1.411, 1.003, 2.144, 1.986, 0.21, 2.035, 2.19, 2.175, 0.945, 2.476, 3.379, 2.456, 0.864, 1.564, 2.346, 1.353, -0.158, 2.884, 1.171, 1.426, 3.504, 1.226, 2.846, 0.739, 1.645)
dist2 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
test_data <- data.frame(norm=norm0, bimodal=bimodal0)
test_data2 <- data.frame(dist=factor(dist2, labels=c("norm", "bimodal")), value=value2)
str(test_data)
## 'data.frame': 200 obs. of 2 variables:
## $ norm : num -0.56 -0.23 1.56 0.07 0.13 1.72 0.46 -1.27 -0.69 -0.45 ...
## $ bimodal: num 0.2 -0.69 -2.27 -1.46 -2.41 -2.48 -2.79 -2.59 -0.35 -2.05 ...
str(test_data2)
## 'data.frame': 400 obs. of 2 variables:
## $ dist : Factor w/ 2 levels "norm","bimodal": 1 1 1 1 1 1 1 1 1 1 ...
## $ value: num -0.56 -0.23 1.559 0.071 0.129 ...
# Plot with test_data
ggplot(test_data, aes(x = norm)) +
geom_rug() +
geom_density()
# Plot two distributions with test_data2
ggplot(test_data2, aes(x = value, fill = dist, col = dist)) +
geom_rug(alpha=0.6) +
geom_density(alpha=0.6)
data(msleep); mammals <- msleep[!is.na(msleep$vore), c("vore", "sleep_total")]
# Individual densities
ggplot(mammals[mammals$vore == "insecti", ], aes(x = sleep_total, fill = vore)) +
geom_density(col = NA, alpha = 0.35) +
scale_x_continuous(limits = c(0, 24)) +
coord_cartesian(ylim = c(0, 0.3))
# With faceting
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
geom_density(col = NA, alpha = 0.35) +
scale_x_continuous(limits = c(0, 24)) +
coord_cartesian(ylim = c(0, 0.3)) +
facet_wrap(~ vore, nrow=2)
# Note that by default, the x ranges fill the scale
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
geom_density(col = NA, alpha = 0.35) +
scale_x_continuous(limits = c(0, 24)) +
coord_cartesian(ylim = c(0, 0.3))
# Trim each density plot individually
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
geom_density(col = NA, alpha = 0.35, trim=TRUE) +
scale_x_continuous(limits=c(0,24)) +
coord_cartesian(ylim = c(0, 0.3))
# Density plot from before
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
geom_density(col = NA, alpha = 0.35) +
scale_x_continuous(limits = c(0, 24)) +
coord_cartesian(ylim = c(0, 0.3))
# Finish the dplyr command
library(dplyr)
mammals2 <- mammals %>%
group_by(vore) %>%
mutate(n=n()/nrow(mammals))
# Density plot, weighted
ggplot(mammals2, aes(x = sleep_total, fill = vore)) +
geom_density(aes(weight = n), col=NA, alpha = 0.35) +
scale_x_continuous(limits = c(0, 24)) +
coord_cartesian(ylim = c(0, 0.3))
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
# Violin plot
ggplot(mammals, aes(x = vore, y = sleep_total, fill = vore)) +
geom_violin()
# Violin plot, weighted
ggplot(mammals2, aes(x = vore, y = sleep_total, fill = vore)) +
geom_violin(aes(weight = n), col=NA)
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density
data(faithful)
# Base layers
p <- ggplot(faithful, aes(x = waiting, y = eruptions)) +
scale_y_continuous(limits = c(1, 5.5), expand = c(0, 0)) +
scale_x_continuous(limits = c(40, 100), expand = c(0, 0)) +
coord_fixed(60 / 4.5)
# Use geom_density_2d()
p + geom_density_2d()
# Use stat_density_2d()
p + stat_density_2d(h=c(5, 0.5), aes(col=..level..))
# Load in the viridis package
library(viridis)
## Warning: package 'viridis' was built under R version 3.2.5
# Load in the viridis package
library(viridis)
# Add viridis color scale
ggplot(faithful, aes(x = waiting, y = eruptions)) +
scale_y_continuous(limits = c(1, 5.5), expand = c(0,0)) +
scale_x_continuous(limits = c(40, 100), expand = c(0,0)) +
coord_fixed(60/4.5) +
stat_density_2d(geom = "tile", aes(fill = ..density..), h=c(5,.5), contour = FALSE) +
scale_fill_viridis()
Defining largeness of a dataset - # observations, # variables, etc. For many observations (e.g., “diamonds” dataset) - can adjust point size, alpha blending, 2-d contours, etc.:
For many variables, can use pre-processing such as PCA:
Ternary plot - triangle plot where all the components add to 100% (e.g., soil composition):
Network plots (e.g., blood type donors):
Diagnostic plots:
Example code includes:
# pairs
data(iris)
pairs(iris[, 1:4])
# chart.Correlation
library(PerformanceAnalytics)
## Warning: package 'PerformanceAnalytics' was built under R version 3.2.5
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
chart.Correlation(iris[, 1:4])
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter
## Warning in plot.window(...): "method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter
## Warning in title(...): "method" is not a graphical parameter
# ggpairs
library(GGally)
data(mtcars); mtcars_fact <- mtcars; mtcars_fact$cyl <- as.factor(mtcars_fact$cyl)
ggpairs(mtcars_fact[, 1:3])
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
library(ggplot2)
library(reshape2)
##
## Attaching package: 'reshape2'
## The following objects are masked from 'package:data.table':
##
## dcast, melt
cor_list <- function(x) {
L <- M <- cor(x)
M[lower.tri(M, diag = TRUE)] <- NA
M <- melt(M)
names(M)[3] <- "points"
L[upper.tri(L, diag = TRUE)] <- NA
L <- melt(L)
names(L)[3] <- "labels"
merge(M, L)
}
# Calculate xx with cor_list
library(dplyr)
xx <- iris %>%
group_by(Species) %>%
do(cor_list(.[1:4]))
# Finish the plot
ggplot(xx, aes(x = Var1, y = Var2)) +
geom_point(aes(col = points, size = abs(points)), shape = 16) +
geom_text(aes(col = labels, size = labels, label = round(labels, 2))) +
scale_size(range = c(0, 6)) +
scale_color_gradient("r", limits = c(-1, 1)) +
scale_y_discrete("", limits = rev(levels(xx$Var1))) +
scale_x_discrete("") +
guides(size = FALSE) +
geom_abline(slope = -1, intercept = nlevels(xx$Var1) + 1) +
coord_fixed() +
facet_grid(. ~ Species) +
theme(axis.text.y = element_text(angle = 45, hjust = 1),
axis.text.x = element_text(angle = 45, hjust = 1),
strip.background = element_blank())
## Warning: Removed 30 rows containing missing values (geom_point).
## Warning: Removed 30 rows containing missing values (geom_text).
# Explore africa
library(GSIF)
## Warning: package 'GSIF' was built under R version 3.2.5
## GSIF version 0.5-3 (2016-07-18)
## URL: http://gsif.r-forge.r-project.org/
data(afsp)
str(afsp)
## List of 2
## $ sites :'data.frame': 26270 obs. of 9 variables:
## ..$ SOURCEID: Factor w/ 26270 levels "100902","100903",..: 5606 5607 5608 5609 5610 5611 5604 5605 5681 5682 ...
## ..$ SOURCEDB: Factor w/ 7 levels "Af_LDSF","Af_soilspec",..: 2 2 2 2 2 2 2 2 2 2 ...
## ..$ LONWGS84: num [1:26270] 36.1 36.1 36.1 36.1 36.1 ...
## ..$ LATWGS84: num [1:26270] -6.93 -6.93 -6.93 -6.93 -6.93 ...
## ..$ TIMESTRR: Date[1:26270], format: "2012-10-02" ...
## ..$ TAXGWRB : Factor w/ 40 levels "AC","AL","Alisol",..: NA NA NA NA NA NA NA NA NA NA ...
## ..$ TAXNUSDA: Factor w/ 960 levels "\"Plinthic\" Udoxic Dystropept",..: NA NA NA NA NA NA NA NA NA NA ...
## ..$ BDRICM : num [1:26270] NA NA NA NA NA NA NA NA NA NA ...
## ..$ DRAINFAO: Factor w/ 7 levels "E","I","M","P",..: NA NA NA NA NA NA NA NA NA NA ...
## $ horizons:'data.frame': 87693 obs. of 14 variables:
## ..$ SOURCEID: Factor w/ 26270 levels "100902","100903",..: 5606 5606 5607 5607 5607 5607 5608 5608 5609 5609 ...
## ..$ UHDICM : num [1:87693] 0 20 0 20 0 20 0 20 0 20 ...
## ..$ LHDICM : num [1:87693] 20 50 20 50 20 50 20 50 20 50 ...
## ..$ MCOMNS : Factor w/ 289 levels "10BG4/1","10R2.5/1",..: NA NA NA NA NA NA NA NA NA NA ...
## ..$ ORCDRC : num [1:87693] 4.36 3.69 3.67 4 5.25 4.57 6.02 3.6 3.33 5.33 ...
## ..$ PHIHOX : num [1:87693] 7.23 7.3 7.06 6.95 7.21 7.25 7.35 7.44 7.26 6.94 ...
## ..$ SNDPPT : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
## ..$ SLTPPT : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
## ..$ CLYPPT : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
## ..$ CRFVOL : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
## ..$ BLD : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
## ..$ CEC : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
## ..$ NTO : num [1:87693] 0.381 0.334 0.318 0.366 0.439 0.404 0.512 0.334 0.329 0.482 ...
## ..$ EMGX : num [1:87693] 2.66 2.95 2.73 3.84 2.36 3.16 3.59 4.37 3.71 2.76 ...
africa <- afsp$horizons[, c("SNDPPT", "SLTPPT", "CLYPPT")]
africa <- africa[complete.cases(africa), ]
africa <- africa / rowSums(africa)
africa <- africa %>% rename(Sand=SNDPPT, Silt=SLTPPT, Clay=CLYPPT)
str(africa)
## 'data.frame': 53830 obs. of 3 variables:
## $ Sand: num 0.845 0.838 0.694 0.661 0.647 ...
## $ Silt: num 0.0326 0.0313 0.0387 0.0782 0.1176 ...
## $ Clay: num 0.122 0.131 0.268 0.261 0.235 ...
# Sample the dataset
africa_sample <- africa[sample(1:nrow(africa), size = 50),]
# Add an ID column from the row.names
africa_sample$ID <- row.names(africa_sample)
# Gather africa_sample
library(tidyr)
africa_sample_tidy <- gather(africa_sample, key, value, -ID)
# Finish the ggplot command
ggplot(africa_sample_tidy, aes(x = factor(ID), y = value, fill = key)) +
geom_bar(stat = "identity") +
coord_flip() +
scale_x_discrete(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0)) +
labs(x = "Location", y = "Composition", fill = "Component") +
theme_minimal()
# Load ggtern
library(ggtern)
## Warning: package 'ggtern' was built under R version 3.2.5
## --
## Consider donating at: http://ggtern.com
## Even small amounts (say $10-50) are very much appreciated!
## Remember to cite, run citation(package = 'ggtern') for further info.
## --
##
## Attaching package: 'ggtern'
## The following objects are masked from 'package:ggplot2':
##
## %+%, aes, annotate, calc_element, ggplot, ggplot_build,
## ggplot_gtable, ggplotGrob, ggsave, layer_data, theme,
## theme_bw, theme_classic, theme_dark, theme_gray, theme_light,
## theme_linedraw, theme_minimal, theme_void
# Build ternary plot
# ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
# geom_point(shape=16, alpha=0.2)
# ggtern and ggplot2 are loaded
# Plot 1
# ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
# geom_density_tern()
# Plot 2
# ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
# stat_density_tern(geom="polygon", aes(fill = ..level.., alpha = ..level..),
# guides(fill = guide_legend(show = FALSE))
# )
# Load geomnet
library(geomnet)
## Warning: package 'geomnet' was built under R version 3.2.5
# Examine structure of madmen
str(geomnet::madmen)
## List of 2
## $ edges :'data.frame': 39 obs. of 2 variables:
## ..$ Name1: Factor w/ 9 levels "Betty Draper",..: 1 1 2 2 2 2 2 2 2 2 ...
## ..$ Name2: Factor w/ 39 levels "Abe Drexler",..: 15 31 2 4 5 6 8 9 11 21 ...
## $ vertices:'data.frame': 45 obs. of 2 variables:
## ..$ label : Factor w/ 45 levels "Abe Drexler",..: 5 9 16 23 26 32 33 38 39 17 ...
## ..$ Gender: Factor w/ 2 levels "female","male": 1 2 2 1 2 1 2 2 2 2 ...
# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
by.x = "Name1", by.y="label",
all = TRUE)
# Examin structure of mmnet
str(mmnet)
## 'data.frame': 75 obs. of 3 variables:
## $ Name1 : Factor w/ 45 levels "Betty Draper",..: 1 1 2 2 2 2 2 2 2 2 ...
## $ Name2 : Factor w/ 39 levels "Abe Drexler",..: 15 31 2 4 5 6 8 9 11 21 ...
## $ Gender: Factor w/ 2 levels "female","male": 1 1 2 2 2 2 2 2 2 2 ...
# geomnet is pre-loaded
# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
by.x = "Name1", by.y = "label",
all = TRUE)
# Finish the ggplot command
# ggplot(data = mmnet, aes(from_id = Name1, to_id = Name2)) +
# geom_net(aes(col=Gender), size=6, linewidth=1, label=TRUE, fontsize=3, labelcolour="black")
# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
by.x = "Name1", by.y = "label",
all = TRUE)
# Tweak the network plot
# ggplot(data = mmnet, aes(from_id = Name1, to_id = Name2)) +
# geom_net(aes(col = Gender),
# size = 6,
# linewidth = 1,
# label = TRUE,
# fontsize = 3,
# labelcolour = "black",
# directed=TRUE) +
# scale_color_manual(values = c("#FF69B4", "#0099ff")) +
# xlim(c(-0.05, 1.05)) +
# ggmap::theme_nothing(legend=TRUE) +
# theme(legend.key=element_blank())
# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
by.x = "Name1", by.y = "label",
all = TRUE)
# Create linear model: res
data(trees)
res <- lm(Volume ~ Girth, data = trees)
# Plot res
par(mfrow = c(2, 2))
plot(res)
par(mfrow = c(1, 1))
# Import ggfortify and use autoplot()
library(ggfortify)
## Warning: package 'ggfortify' was built under R version 3.2.5
autoplot(res, ncol=2)
# Inspect structure of Canada
library(vars); data(Canada)
## Warning: package 'vars' was built under R version 3.2.5
## Loading required package: MASS
##
## Attaching package: 'MASS'
## The following object is masked _by_ '.GlobalEnv':
##
## mammals
## The following object is masked from 'package:dplyr':
##
## select
## Loading required package: strucchange
## Warning: package 'strucchange' was built under R version 3.2.4
## Loading required package: sandwich
## Loading required package: urca
## Warning: package 'urca' was built under R version 3.2.5
## Loading required package: lmtest
## Warning: package 'lmtest' was built under R version 3.2.5
str(Canada)
## mts [1:84, 1:4] 930 930 930 931 933 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:4] "e" "prod" "rw" "U"
## - attr(*, "tsp")= num [1:3] 1980 2001 4
## - attr(*, "class")= chr [1:2] "mts" "ts"
# Call plot() on Canada
plot(Canada)
# Call autoplot() on Canada
# autoplot(Canada)
# ggfortify and eurodist are available
# Autoplot + ggplot2 tweaking
# autoplot(eurodist) +
# labs(x="", y="") +
# coord_fixed() +
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
# Autoplot of MDS
# autoplot(cmdscale(eurodist, eig=TRUE), label=TRUE, label.size=3, size=0)
# perform clustering
iris_k <- kmeans(iris[-5], centers=3)
# autplot: coloring according to cluster
# autoplot(iris_k, data=iris, frame=TRUE)
# autoplot: coloring according to species
# autoplot(iris_k, data=iris, frame=TRUE, col="Species")
Choropleths are a series of polygons (or points or lines), and are useful when you have the shapes file and some underlying data. Maps (e.g., full US or by state or etc.) are a primary example - can put right in to ggplot2:
Cartographic maps are an alternative (topographical, photographic, etc.):
Animations can be useful for dense, temporal data or as an exploratory tool:
Example code includes:
library(ggplot2)
library(ggmap)
## Warning: package 'ggmap' was built under R version 3.2.5
library(ggthemes)
library(maps)
## Warning: package 'maps' was built under R version 3.2.5
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
library(viridis)
# Basic map of the USA
usa <- ggplot2::map_data("usa")
str(usa)
## 'data.frame': 7243 obs. of 6 variables:
## $ long : num -101 -101 -101 -101 -101 ...
## $ lat : num 29.7 29.7 29.7 29.6 29.6 ...
## $ group : num 1 1 1 1 1 1 1 1 1 1 ...
## $ order : int 1 2 3 4 5 6 7 8 9 10 ...
## $ region : chr "main" "main" "main" "main" ...
## $ subregion: chr NA NA NA NA ...
ggplot(data=usa, aes(x=long, y=lat, group=group)) +
geom_polygon() +
coord_map()
# Add USA cities to the USA map (continental US only, cities with 250k+ population)
library(dplyr)
data(us.cities)
cities <- us.cities %>%
mutate(City=name, State=country.etc, Pop_est=pop) %>%
dplyr::select(City, State, Pop_est, lat, long) %>%
filter(!(State %in% c("AK", "HI", "ma")) & Pop_est >= 250000)
ggplot(usa, aes(x = long, y = lat, group = group)) +
geom_polygon() +
geom_point(data=cities, aes(group=State, size=Pop_est, col="red"), shape=16, alpha=0.6) +
coord_map() +
theme_map()
# Arrange cities
cities_arr <- arrange(cities, Pop_est)
# Create US plot of cities, colored by the viridis theme
ggplot(usa, aes(x = long, y = lat, group = group)) +
geom_polygon(fill="grey90") +
geom_point(data=cities_arr, aes(group=State, col=Pop_est), shape=16, size=2, alpha=0.6) +
coord_map() +
theme_map() +
scale_color_viridis()
# Create a dataset of populations by state
st_data <- "california ; texas ; florida ; new york ; illinois ; pennsylvania ; ohio ; georgia ; north carolina ; michigan ; new jersey ; virginia ; washington ; arizona ; massachusetts ; indiana ; tennessee ; missouri ; maryland ; wisconsin ; minnesota ; colorado ; south carolina ; alabama ; louisiana ; kentucky ; oregon ; oklahoma ; connecticut ; puerto rico ; iowa ; utah ; mississippi ; arkansas ; kansas ; nevada ; new mexico ; nebraska ; west virginia ; idaho ; hawaii ; new hampshire ; maine ; rhode island ; montana ; delaware ; south dakota ; north dakota ; alaska ; district of columbia ; vermont ; wyoming"
pop_data <- "39144818 ; 27469114 ; 20271272 ; 19795791 ; 12859995 ; 12802503 ; 11613423 ; 10214860 ; 10042802 ; 9922576 ; 8958013 ; 8382993 ; 7170351 ; 6828065 ; 6794422 ; 6619680 ; 6600299 ; 6083672 ; 6006401 ; 5771337 ; 5489594 ; 5456574 ; 4896146 ; 4858979 ; 4670724 ; 4425092 ; 4028977 ; 3911338 ; 3590886 ; 3474182 ; 3123899 ; 2995919 ; 2992333 ; 2978204 ; 2911641 ; 2890845 ; 2085109 ; 1896190 ; 1844128 ; 1654930 ; 1431603 ; 1330608 ; 1329328 ; 1056298 ; 1032949 ; 945934 ; 858469 ; 756927 ; 738432 ; 672228 ; 626042 ; 586107"
pop <- data.frame(region=strsplit(st_data, split=" ; ")[[1]],
Pop_est=as.numeric(strsplit(pop_data, split=" ; ")[[1]]),
stringsAsFactors = FALSE
)
# Map the basic state data
state <- map_data("state")
ggplot(data=state, aes(x=long, y=lat, group=group, fill=region)) +
geom_polygon(col="white") +
coord_map()
# Map the states by population
state2 <- merge(state, pop, by="region")
ggplot(data=state2, aes(x=long, y=lat, group=group, fill=Pop_est)) +
geom_polygon(col="white") +
coord_map() +
theme_map()
# Import shape information: germany (commented out since files not available)
library(rgdal)
## Warning: package 'rgdal' was built under R version 3.2.5
## Loading required package: sp
## Warning: package 'sp' was built under R version 3.2.5
## rgdal: version: 1.2-3, (SVN revision 639)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.0.1, released 2015/09/15
## Path to GDAL shared files: C:/Users/Dave/Documents/R/win-library/3.2/rgdal/gdal
## GDAL does not use iconv for recoding strings.
## Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
## Path to PROJ.4 shared files: C:/Users/Dave/Documents/R/win-library/3.2/rgdal/proj
## Linking to sp version: 1.2-3
# germany <- readOGR(dsn="shapes", "DEU_adm1")
# bundes <- fortify(germany)
#
# ggplot(data=bundes, aes(x=long, y=lat, group=group)) +
# geom_polygon(fill="blue", col="white") +
# coord_map() +
# theme_nothing()
#
# bundes$state <- factor(as.numeric(bundes$id))
# levels(bundes$state) <- germany$NAME_1
#
# bundes_unemp <- merge(bundes, unemp, by="state")
#
# ggplot(bundes_unemp, aes(x = long, y = lat, group = group, fill=unemployment)) +
# geom_polygon() +
# coord_map() +
# theme_map()
# Create the map of London
library(ggmap)
london_map_13 <- get_map("London, England", zoom=13)
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=London,+England&zoom=13&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=London,%20England&sensor=false
ggmap(london_map_13)
# Experiment with get_map() and use ggmap() to plot it!
# temp1 <- get_map("London, England", zoom=13, maptype="toner", source="stamen")
# ggmap(temp1)
temp2 <- get_map("London, England", zoom=13, maptype="hybrid")
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=London,+England&zoom=13&size=640x640&scale=2&maptype=hybrid&language=en-EN&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=London,%20England&sensor=false
ggmap(temp2)
# Map some key sites in London
london_sites <- strsplit("Tower of London, London ; Buckingham Palace, London ; Tower Bridge, London ; Queen Elizabeth Olympic Park, London", " ; ")[[1]]
xx <- geocode(london_sites)
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20of%20London,%20London&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Buckingham%20Palace,%20London&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20Bridge,%20London&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Queen%20Elizabeth%20Olympic%20Park,%20London&sensor=false
xx$location <- sub(", London", "", london_sites)
london_ton_13 <- get_map(location = "London, England", zoom = 13,
source = "stamen", maptype = "toner")
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=London,+England&zoom=13&size=640x640&scale=2&maptype=terrain&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=London,%20England&sensor=false
## Map from URL : http://tile.stamen.com/toner/13/4091/2722.png
## Map from URL : http://tile.stamen.com/toner/13/4092/2722.png
## Map from URL : http://tile.stamen.com/toner/13/4093/2722.png
## Map from URL : http://tile.stamen.com/toner/13/4094/2722.png
## Map from URL : http://tile.stamen.com/toner/13/4091/2723.png
## Map from URL : http://tile.stamen.com/toner/13/4092/2723.png
## Map from URL : http://tile.stamen.com/toner/13/4093/2723.png
## Map from URL : http://tile.stamen.com/toner/13/4094/2723.png
## Map from URL : http://tile.stamen.com/toner/13/4091/2724.png
## Map from URL : http://tile.stamen.com/toner/13/4092/2724.png
## Map from URL : http://tile.stamen.com/toner/13/4093/2724.png
## Map from URL : http://tile.stamen.com/toner/13/4094/2724.png
## Map from URL : http://tile.stamen.com/toner/13/4091/2725.png
## Map from URL : http://tile.stamen.com/toner/13/4092/2725.png
## Map from URL : http://tile.stamen.com/toner/13/4093/2725.png
## Map from URL : http://tile.stamen.com/toner/13/4094/2725.png
# Add a geom_points layer
ggmap(london_ton_13) +
geom_point(data=xx, aes(col=location), size=6)
## Warning: Removed 1 rows containing missing values (geom_point).
# Expand to use the bounding box
xx <- geocode(london_sites)
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20of%20London,%20London&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Buckingham%20Palace,%20London&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20Bridge,%20London&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Queen%20Elizabeth%20Olympic%20Park,%20London&sensor=false
xx$location <- sub(", London", "", london_sites)
xx$location[4] <- "Queen Elizabeth\nOlympic Park"
# Create bounding box: bbox
bbox <- make_bbox(lon = xx$lon, lat = xx$lat, f = 0.3)
london_ton_13 <- get_map(bbox, zoom = 13,
source = "stamen", maptype = "toner"
)
## Map from URL : http://tile.stamen.com/toner/13/4091/2722.png
## Warning in file.remove(index[[url]]): cannot remove file
## 'a991dd39e80eba942f916d1a39eacba1.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4092/2722.png
## Warning in file.remove(index[[url]]): cannot remove file
## 'ca4d532407f75fb40f356d82e9f3e868.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4093/2722.png
## Warning in file.remove(index[[url]]): cannot remove file
## '2d23913e51259d39586aa2f72cf7262a.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4094/2722.png
## Warning in file.remove(index[[url]]): cannot remove file
## 'd95217086725a265fa36c032fbc90ad6.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4095/2722.png
## Map from URL : http://tile.stamen.com/toner/13/4096/2722.png
## Map from URL : http://tile.stamen.com/toner/13/4091/2723.png
## Warning in file.remove(index[[url]]): cannot remove file
## '34870d377d252bc5a4f1705aaec727a7.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4092/2723.png
## Warning in file.remove(index[[url]]): cannot remove file
## 'afecef47c6d1c2bb613ae817f73ef94e.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4093/2723.png
## Warning in file.remove(index[[url]]): cannot remove file
## 'f0de52ccfe7b807ab9e08f76c37ff7be.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4094/2723.png
## Warning in file.remove(index[[url]]): cannot remove file
## 'f7fedbdcd7d7ca7fa3714dc4a4b6b802.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4095/2723.png
## Map from URL : http://tile.stamen.com/toner/13/4096/2723.png
## Map from URL : http://tile.stamen.com/toner/13/4091/2724.png
## Warning in file.remove(index[[url]]): cannot remove file
## '8bebd0980b74870f3d7dc958d655198f.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4092/2724.png
## Warning in file.remove(index[[url]]): cannot remove file
## '245dcf359e94685490a556539f0ef26f.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4093/2724.png
## Warning in file.remove(index[[url]]): cannot remove file
## '92a8bede90fdcd1ba21c8c78c10f06b3.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4094/2724.png
## Warning in file.remove(index[[url]]): cannot remove file
## '9acc9d919db20a0d68fdef3dd4092cfe.rds', reason 'No such file or directory'
## Map from URL : http://tile.stamen.com/toner/13/4095/2724.png
## Map from URL : http://tile.stamen.com/toner/13/4096/2724.png
# Map from previous exercise
ggmap(london_ton_13) +
geom_point(data = xx, aes(col = location), size = 6)
# New map with labels
ggmap(london_ton_13) +
geom_label(data=xx, aes(label=location), size=4, fontface="bold", fill="grey90", col="#E41A1C")
# Get the map data of "Germany" and Plot map and polygon on top: (not displayed)
# germany_06 <- get_map("Germany", zoom=6)
# ggmap(germany_06) +
# geom_polygon(data=bundes, aes(x=long, y=lat, group=group), fill=NA, col="red") +
# coord_map()
# Animated Japan map (not shown)
# str(japan)
#
# saveGIF({
#
# for (i in unique(japan$time)) {
#
# data <- japan[japan$time == i, ]
#
# p <- ggplot(data, aes(x = AGE, y = POP, fill = SEX, width = 1)) +
# coord_flip() +
# geom_bar(data = data[data$SEX == "Female",], stat = "identity") +
# geom_bar(data = data[data$SEX == "Male",], stat = "identity") +
# ggtitle(i)
#
# print(p)
#
# }
#
# }, movie.name = "pyramid.gif", interval = 0.1)
# Animate the vocabularies by year
# library(gganimate)
# library(car)
# data(Vocab)
# p <- ggplot(Vocab, aes(x = education, y = vocabulary,
# color = year, group = year,
# frame=year, cumulative=TRUE
# )
# ) +
# stat_smooth(method = "lm", se = FALSE, size = 3)
#
# gg_animate(p, filename="vocab.gif", interval = 1.0)
The ggplot2 internals include 2 plotting systems in R (base and grid):
There are many possible customizations to the grid process:
The ggplot2 package is built using the grid() framework:
Any time you create a ggplot (even if empty - ggplot()), you create an object:
The gridExtra library allows for additional customization:
You can write your own extensions in ggplot2 2.0 (e.g., the “bag plot” based on Tukey):
Case Study - weather, using NYC data and several Tukey-like graphs:
Case Study Part II - same idea, but increasingly efficient by way of several stat functions in ggplot2:
Example code includes:
library(Hmisc)
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:GSIF':
##
## describe
## The following objects are masked from 'package:dplyr':
##
## combine, src, summarize
## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units
library(grid)
library(gtable)
## Warning: package 'gtable' was built under R version 3.2.5
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following objects are masked from 'package:ggtern':
##
## arrangeGrob, grid.arrange
library(dplyr)
detach(package:dplyr)
library(plyr)
## -------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## -------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:Hmisc':
##
## is.discrete, summarize
## The following object is masked from 'package:maps':
##
## ozone
## The following object is masked from 'package:purrr':
##
## compact
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:Hmisc':
##
## combine, src, summarize
## The following object is masked from 'package:MASS':
##
## select
## The following object is masked from 'package:GGally':
##
## nasa
## The following objects are masked from 'package:xts':
##
## first, last
## The following objects are masked from 'package:data.table':
##
## between, last
## The following object is masked from 'package:purrr':
##
## order_by
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(aplpack)
## Warning: package 'aplpack' was built under R version 3.2.5
## Loading required package: tcltk
# Draw rectangle in null viewport
grid.rect(gp=gpar(fill = "grey90"))
# Write text in null viewport
grid.text("null viewport")
# Draw a line
grid.lines(x=c(0, 0.75), y=c(0.25, 1), gp=gpar(lty=2, col="red"))
# Populate null viewport
grid.rect(gp = gpar(fill = "grey90"))
grid.text("null viewport")
grid.lines(x = c(0,0.75), y = c(0.25, 1),
gp = gpar(lty = 2, col = "red"))
# Create new viewport: vp
vp <- viewport(x=0.5, y=0.5, width=0.5, height=0.5, just="center")
# Push vp
pushViewport(vp)
# Populate new viewport with rectangle
grid.rect(gp=gpar(fill="blue"))
# Create plot viewport: pvp
mar <- c(5, 4, 2, 2)
pvp <- plotViewport(mar)
# Push pvp
pushViewport(pvp)
# Add rectangle
grid.rect(gp=gpar(fill="grey80"))
# Create data viewport: dvp
dvp <- dataViewport(mtcars$wt, mtcars$mpg)
# Push dvp
pushViewport(dvp)
# Add two axes
grid.xaxis()
grid.yaxis()
# Work from before
pushViewport(plotViewport(c(5, 4, 2, 2)))
grid.rect(gp = gpar())
pushViewport(dataViewport(xData = mtcars$wt, yData = mtcars$mpg))
grid.xaxis()
grid.yaxis()
# Add text to x axis
grid.text(label="Weight", y=unit(-3, "lines"))
# Add text to y axis
grid.text(label="MPG", x=unit(-3, "lines"), rot=90)
# Add points
grid.points(x=mtcars$wt, y=mtcars$mpg, pch=16)
# Work from before
pushViewport(plotViewport(c(5, 4, 2, 2)))
grid.rect(gp = gpar())
pushViewport(dataViewport(xData = mtcars$wt, yData = mtcars$mpg))
grid.xaxis()
grid.yaxis()
# Work from before - add names
grid.text("Weight", y = unit(-3, "lines"), name = "xaxis")
grid.text("MPG", x = unit(-3, "lines"), rot = 90, name = "yaxis")
grid.points(x = mtcars$wt, y = mtcars$mpg, pch = 16, name = "datapoints")
# Edit "xaxis"
grid.edit("xaxis", label="Miles/(US) gallon")
# Edit "yaxis"
grid.edit("yaxis", label="Weight (1000 lbs)")
# Edit "datapoints"
grid.edit("datapoints", gp=gpar(col="#C3212766", cex=2))
# A simple plot p
p <- ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) + geom_point()
# Create gtab with ggplotGrob()
gtab <- ggplotGrob(p)
# Print out gtab
gtab
## TableGrob (10 x 9) "layout": 18 grobs
## z cells name grob
## 1 0 ( 1-10, 1- 9) background rect[plot.background..rect.15947]
## 2 5 ( 5- 5, 3- 3) spacer zeroGrob[NULL]
## 3 7 ( 6- 6, 3- 3) axis-l absoluteGrob[GRID.absoluteGrob.15923]
## 4 3 ( 7- 7, 3- 3) spacer zeroGrob[NULL]
## 5 6 ( 5- 5, 4- 4) axis-t zeroGrob[NULL]
## 6 1 ( 6- 6, 4- 4) panel gTree[panel-1.gTree.15903]
## 7 9 ( 7- 7, 4- 4) axis-b absoluteGrob[GRID.absoluteGrob.15916]
## 8 4 ( 5- 5, 5- 5) spacer zeroGrob[NULL]
## 9 8 ( 6- 6, 5- 5) axis-r zeroGrob[NULL]
## 10 2 ( 7- 7, 5- 5) spacer zeroGrob[NULL]
## 11 10 ( 4- 4, 4- 4) xlab-t zeroGrob[NULL]
## 12 11 ( 8- 8, 4- 4) xlab-b titleGrob[axis.title.x..titleGrob.15906]
## 13 12 ( 6- 6, 2- 2) ylab-l titleGrob[axis.title.y..titleGrob.15909]
## 14 13 ( 6- 6, 6- 6) ylab-r zeroGrob[NULL]
## 15 14 ( 6- 6, 8- 8) guide-box gtable[guide-box]
## 16 15 ( 3- 3, 4- 4) subtitle zeroGrob[plot.subtitle..zeroGrob.15944]
## 17 16 ( 2- 2, 4- 4) title zeroGrob[plot.title..zeroGrob.15943]
## 18 17 ( 9- 9, 4- 4) caption zeroGrob[plot.caption..zeroGrob.15945]
# Extract the grobs from gtab: gtab
g <- gtab$grobs
# Draw only the legend
grid.draw(g[[15]])
# Code from before
p <- ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) + geom_point()
gtab <- ggplotGrob(p)
g <- gtab$grobs
grid.draw(g[[15]])
# Show layout of g[[15]]
gtable_show_layout(g[[15]])
# Create text grob
my_text <- textGrob(label = "Motor Trend, 1974", gp = gpar(fontsize = 7, col = "gray25"))
# Use gtable_add_grob to modify original gtab
new_legend <- gtable_add_grob(gtab$grobs[[15]], my_text, 3, 2)
# Update in gtab
gtab$grobs[[15]] <- new_legend
# Draw gtab
grid.draw(gtab)
# Simple plot p
p <- ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) + geom_point()
# Examine class() and names()
class(p)
## [1] "gg" "ggplot"
names(p)
## [1] "data" "layers" "scales" "mapping" "theme"
## [6] "coordinates" "facet" "plot_env" "labels"
# Print the scales sub-list
p$scales$scales
## list()
# Update p
p <- p +
scale_x_continuous("Length", limits = c(4, 8), expand = c(0, 0)) +
scale_y_continuous("Width", limits = c(2, 4.5), expand = c(0, 0))
# Print the scales sub-list
p$scales$scales
## [[1]]
## <ScaleContinuousPosition>
## Range:
## Limits: 4 -- 8
##
## [[2]]
## <ScaleContinuousPosition>
## Range:
## Limits: 2 -- 4.5
# Box plot of mtcars: p
p <- ggplot(mtcars, aes(x = factor(cyl), y = wt)) + geom_boxplot()
# Create pbuild
pbuild <- ggplot_build(p)
# a list of 3 elements
names(pbuild)
## [1] "data" "layout" "plot"
# Print out each element in pbuild
pbuild$data
## [[1]]
## ymin lower middle upper ymax outliers notchupper
## 1 1.513 1.8850 2.200 2.62250 3.19 2.551336
## 2 2.620 2.8225 3.215 3.44000 3.46 3.583761
## 3 3.170 3.5325 3.755 4.01375 4.07 5.250, 5.424, 5.345 3.958219
## notchlower x PANEL group ymin_final ymax_final xmin xmax weight colour
## 1 1.848664 1 1 1 1.513 3.190 0.625 1.375 1 grey20
## 2 2.846239 2 1 2 2.620 3.460 1.625 2.375 1 grey20
## 3 3.551781 3 1 3 3.170 5.424 2.625 3.375 1 grey20
## fill size alpha shape linetype
## 1 white 0.5 NA 19 solid
## 2 white 0.5 NA 19 solid
## 3 white 0.5 NA 19 solid
pbuild$layout
## <ggproto object: Class Layout>
## facet: <ggproto object: Class FacetNull, Facet>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map: function
## map_data: function
## params: list
## render_back: function
## render_front: function
## render_panels: function
## setup_data: function
## setup_params: function
## shrink: TRUE
## train: function
## train_positions: function
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetNull, Facet>
## finish_data: function
## get_scales: function
## map: function
## map_position: function
## panel_layout: data.frame
## panel_ranges: list
## panel_scales: list
## render: function
## render_labels: function
## reset_scales: function
## setup: function
## train_position: function
## train_ranges: function
## xlabel: function
## ylabel: function
## super: <ggproto object: Class Layout>
pbuild$plot
# Create gtab from pbuild
gtab <- ggplot_gtable(pbuild)
# Draw gtab
grid.draw(gtab)
# Box plot of mtcars: p
p <- ggplot(mtcars, aes(x = factor(cyl), y = wt)) + geom_boxplot()
# Build pdata
pdata <- ggplot_build(p)$data
# Access the first element of the list, a data frame
class(pdata[[1]])
## [1] "data.frame"
# Isolate this data frame
my_df <- pdata[[1]]
# The x labels
my_df$group <- c("4", "6", "8")
# Print out specific variables
my_df[c(1:6, 11)]
## ymin lower middle upper ymax outliers group
## 1 1.513 1.8850 2.200 2.62250 3.19 4
## 2 2.620 2.8225 3.215 3.44000 3.46 6
## 3 3.170 3.5325 3.755 4.01375 4.07 5.250, 5.424, 5.345 8
# Add a theme (legend at the bottom)
g1 <- ggplot(mtcars, aes(wt, mpg, col = cyl)) +
geom_point(alpha = 0.5) +
theme(legend.position="bottom")
# Add a theme (no legend)
g2 <- ggplot(mtcars, aes(disp, fill = cyl)) +
geom_histogram(position = "identity", alpha = 0.5, binwidth = 20) +
theme(legend.position="none")
# Load gridExtra
library(gridExtra)
# Call grid.arrange()
grid.arrange(g1, g2, ncol=2)
# Definitions of g1 and g2
g1 <- ggplot(mtcars, aes(wt, mpg, col = cyl)) +
geom_point() +
theme(legend.position = "bottom")
g2 <- ggplot(mtcars, aes(disp, fill = cyl)) +
geom_histogram(binwidth = 20) +
theme(legend.position = "none")
# Extract the legend from g1
my_legend <- ggplotGrob(g1)$grobs[[15]]
# Create g1_noleg
g1_noleg <- ggplot(mtcars, aes(wt, mpg, col = cyl)) +
geom_point() +
theme(legend.position = "none")
# Calculate the height: legend_height
legend_height <- sum(my_legend$heights)
# Arrange g1_noleg, g2 and my_legend
grid.arrange(g1_noleg, g2, my_legend,
layout_matrix=matrix(c(1, 3, 2, 3), ncol=2),
heights=unit.c(unit(1, "npc") - legend_height, legend_height)
)
Continuing with:
test_data <- data.frame(x=rep(0, 60), y=rep(0, 60))
test_data$x <- as.numeric(strsplit("2560 ; 2345 ; 1845 ; 2260 ; 2440 ; 2285 ; 2275 ; 2350 ; 2295 ; 1900 ; 2390 ; 2075 ; 2330 ; 3320 ; 2885 ; 3310 ; 2695 ; 2170 ; 2710 ; 2775 ; 2840 ; 2485 ; 2670 ; 2640 ; 2655 ; 3065 ; 2750 ; 2920 ; 2780 ; 2745 ; 3110 ; 2920 ; 2645 ; 2575 ; 2935 ; 2920 ; 2985 ; 3265 ; 2880 ; 2975 ; 3450 ; 3145 ; 3190 ; 3610 ; 2885 ; 3480 ; 3200 ; 2765 ; 3220 ; 3480 ; 3325 ; 3855 ; 3850 ; 3195 ; 3735 ; 3665 ; 3735 ; 3415 ; 3185 ; 3690", " ; ")[[1]])
test_data$y <- as.numeric(strsplit("97 ; 114 ; 81 ; 91 ; 113 ; 97 ; 97 ; 98 ; 109 ; 73 ; 97 ; 89 ; 109 ; 305 ; 153 ; 302 ; 133 ; 97 ; 125 ; 146 ; 107 ; 109 ; 121 ; 151 ; 133 ; 181 ; 141 ; 132 ; 133 ; 122 ; 181 ; 146 ; 151 ; 116 ; 135 ; 122 ; 141 ; 163 ; 151 ; 153 ; 202 ; 180 ; 182 ; 232 ; 143 ; 180 ; 180 ; 151 ; 189 ; 180 ; 231 ; 305 ; 302 ; 151 ; 202 ; 182 ; 181 ; 143 ; 146 ; 146", " ; ")[[1]])
# Call bagplot() on test_data
bagplot(test_data)
# Call compute.bagplot on test_data: bag
bag <- compute.bagplot(test_data)
# Highlight components
points(bag$hull.loop, col = "green", pch = 16)
points(bag$hull.bag, col = "orange", pch = 16)
points(bag$pxy.outlier, col="purple", pch=16)
# Create data frames from matrices
hull.loop <- data.frame(x = bag$hull.loop[,1], y = bag$hull.loop[,2])
hull.bag <- data.frame(x = bag$hull.bag[,1], y = bag$hull.bag[,2])
pxy.outlier <- data.frame(x = bag$pxy.outlier[, 1], y=bag$pxy.outlier[, 2])
# Finish the ggplot command
ggplot(test_data, aes(x=x, y=y)) +
geom_polygon(data = hull.loop, fill = "green") +
geom_polygon(data = hull.bag, fill = "orange") +
geom_point(data=pxy.outlier, col="purple", pch=16, cex=1.5)
# ggproto for StatLoop (hull.loop)
StatLoop <- ggproto("StatLoop", Stat,
required_aes = c("x", "y"),
compute_group = function(data, scales) {
bag <- compute.bagplot(x = data$x, y = data$y)
data.frame(x = bag$hull.loop[,1], y = bag$hull.loop[,2])
})
# ggproto for StatBag (hull.bag)
StatBag <- ggproto("StatBag", Stat,
required_aes = c("x", "y"),
compute_group = function(data, scales) {
bag <- compute.bagplot(x = data$x, y = data$y)
data.frame(x = bag$hull.bag[, 1], y = bag$hull.bag[, 2])
})
# ggproto for StatOut (pxy.outlier)
StatOut <- ggproto("StatOut", Stat,
required_aes = c("x", "y"),
compute_group = function(data, scales) {
bag <- compute.bagplot(x = data$x, y = data$y)
data.frame(x = bag$pxy.outlier[, 1], y = bag$pxy.outlier[, 2])
})
# Combine ggproto objects in layers to build stat_bag()
stat_bag <- function(mapping = NULL, data = NULL, geom = "polygon",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, loop = FALSE, ...) {
list(
# StatLoop layer
layer(
stat = StatLoop, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, alpha=0.35, col=NA, ...)
),
# StatBag layer
layer(
stat = StatBag, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, alpha=0.35, col=NA, ...)
),
# StatOut layer
layer(
stat = StatOut, data = data, mapping = mapping, geom = "point",
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, alpha = 0.7, col = NA, shape = 21, ...)
)
)
}
# Previous method
ggplot(test_data, aes(x = x, y = y)) +
geom_polygon(data = hull.loop, fill = "green") +
geom_polygon(data = hull.bag, fill = "orange") +
geom_point(data = pxy.outlier, col = "purple", pch = 16, cex = 1.5)
# stat_bag
ggplot(test_data, aes(x = x, y = y)) +
stat_bag(fill = "black")
## Warning: Removed 4 rows containing missing values (geom_point).
# stat_bag on test_data2
# Would require brining over test_data2 (point is that it is now transferrable)
# ggplot(test_data2, aes(x = x, y = y, fill=treatment)) +
# stat_bag()
# Data set NYNEWYOR.txt downloaded from US Cities/New York/NYC available at:
# http://academic.udayton.edu/kissock/http/Weather/
# Import weather data
weather <- read.fwf("NYNEWYOR.txt",
header = FALSE,
col.names = c("month", "day", "year", "temp"),
widths = c(14, 14, 13, 4)
)
# Check structure of weather
str(weather)
## 'data.frame': 8009 obs. of 4 variables:
## $ month: num 1 1 1 1 1 1 1 1 1 1 ...
## $ day : num 1 2 3 4 5 6 7 8 9 10 ...
## $ year : num 1995 1995 1995 1995 1995 ...
## $ temp : num 44 41 28 31 21 27 42 35 34 29 ...
# Create past with two filter() calls
past <- weather %>%
filter(month != 2 | day != 29) %>%
filter(year != max(weather$year))
# Check structure of past
str(past)
## 'data.frame': 7665 obs. of 4 variables:
## $ month: num 1 1 1 1 1 1 1 1 1 1 ...
## $ day : num 1 2 3 4 5 6 7 8 9 10 ...
## $ year : num 1995 1995 1995 1995 1995 ...
## $ temp : num 44 41 28 31 21 27 42 35 34 29 ...
# Create new version of past
past_summ <- past %>%
group_by(year) %>%
mutate(yearday=1:length(day)) %>%
ungroup() %>%
filter(temp != -99) %>%
group_by(yearday) %>%
mutate(max = max(temp),
min = min(temp),
avg = mean(temp),
CI_lower = Hmisc::smean.cl.normal(temp)[2],
CI_upper = Hmisc::smean.cl.normal(temp)[3]
) %>%
ungroup()
# Structure of past_summ
str(past_summ)
## Classes 'tbl_df', 'tbl' and 'data.frame': 7645 obs. of 10 variables:
## $ month : num 1 1 1 1 1 1 1 1 1 1 ...
## $ day : num 1 2 3 4 5 6 7 8 9 10 ...
## $ year : num 1995 1995 1995 1995 1995 ...
## $ temp : num 44 41 28 31 21 27 42 35 34 29 ...
## $ yearday : int 1 2 3 4 5 6 7 8 9 10 ...
## $ max : num 51 48 57 55 56 62 52 57 54 47 ...
## $ min : num 17 15 16 15 21 14 14 12 21 8.5 ...
## $ avg : num 35.6 35.4 34.9 35.1 35.9 ...
## $ CI_lower: num 31 31.6 29.7 29.9 31.9 ...
## $ CI_upper: num 40.1 39.2 40 40.4 39.9 ...
# Adapt historical plot
ggplot(past_summ, aes(x = yearday, y = temp)) +
geom_point(col = "#EED8AE", alpha = 0.3, shape=16) +
geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66")
# Create present
present <- weather %>%
filter(!(month == 2 & day == 29)) %>%
filter(year == max(year)) %>%
group_by(year) %>%
mutate(yearday = 1:length(day)) %>%
ungroup() %>%
filter(temp != -99)
# Add geom_line to ggplot command
ggplot(past_summ, aes(x = yearday, y = temp)) +
geom_point(col = "#EED8AE", alpha = 0.3, shape = 16) +
geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66") +
geom_line(data=present, aes(x = yearday, y=temp))
# Create past_highs
past_highs <- past_summ %>%
group_by(yearday) %>%
summarise(past_high = max(temp))
# Create record_high
record_high <- present %>%
left_join(past_highs, by="yearday") %>%
filter(temp > past_high)
# Add record_high information to plot
ggplot(past_summ, aes(x = yearday, y = temp)) +
geom_point(col = "#EED8AE", alpha = 0.3, shape = 16) +
geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66") +
geom_line(data = present) +
geom_point(data=record_high, col = "#CD2626")
# Create past_extremes
past_extremes <- past_summ %>%
group_by(yearday) %>%
summarise(past_low = min(temp),
past_high = max(temp)
)
# Create record_high_low
record_high_low <- present %>%
left_join(past_extremes, by="yearday") %>%
mutate(record = ifelse(temp < past_low,
"#0000CD",
ifelse(temp > past_high,
"#CD2626",
"#00000000")
)
)
# Structure of record_high_low
str(record_high_low)
## Classes 'tbl_df', 'tbl' and 'data.frame': 338 obs. of 8 variables:
## $ month : num 1 1 1 1 1 1 1 1 1 1 ...
## $ day : num 1 2 3 4 5 6 7 8 9 10 ...
## $ year : num 2016 2016 2016 2016 2016 ...
## $ temp : num 41 37 40 33 19 32 39 40 43 50 ...
## $ yearday : int 1 2 3 4 5 6 7 8 9 10 ...
## $ past_low : num 17 15 16 15 21 14 14 12 21 8.5 ...
## $ past_high: num 51 48 57 55 56 62 52 57 54 47 ...
## $ record : chr "#00000000" "#00000000" "#00000000" "#00000000" ...
# Add point layer of record_high_low
ggplot(past_summ, aes(x = yearday, y = temp)) +
geom_point(col = "#EED8AE", alpha = 0.3, shape = 16) +
geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66") +
geom_line(data = present) +
geom_point(data=record_high_low, aes(col=record)) +
scale_color_identity()
# Finish the function draw_pop_legend
draw_pop_legend <- function(x = 0.6, y = 0.2, width = 0.2, height = 0.2, fontsize = 10) {
# Finish viewport() function
pushViewport(viewport(x = x, y = y, width = width, height = height, just = "center"))
legend_labels <- c("Past record high",
"95% CI range",
"Current year",
"Past years",
"Past record low")
legend_position <- c(0.9, 0.7, 0.5, 0.2, 0.1)
# Finish grid.text() function
grid.text(legend_labels, x = 0.12, y = legend_position,
just = "left", gp = gpar(fontsize = fontsize, col = "grey20"))
# Position dots, rectangle and line
point_position_y <- c(0.1, 0.2, 0.9)
point_position_x <- rep(0.06, length(point_position_y))
grid.points(x = point_position_x, y = point_position_y, pch = 16,
gp = gpar(col = c("#0000CD", "#EED8AE", "#CD2626")))
grid.rect(x = 0.06, y = 0.5, width = 0.06, height = 0.4,
gp = gpar(col = NA, fill = "#8B7E66"))
grid.lines(x = c(0.03, 0.09), y = c(0.5, 0.5),
gp = gpar(col = "black", lwd = 3))
# Add popViewport() for bookkeeping
popViewport()
}
# Call draw_pop_legend()
draw_pop_legend()
# Finish the clean_weather function
clean_weather <- function(file) {
weather <- read.fwf(file,
header=FALSE,
col.names = c("month", "day", "year", "temp"),
widths = c(14, 14, 13, 4))
weather %>%
filter(month != 2 | day != 29) %>%
group_by(year) %>%
mutate(yearday = 1:length(day)) %>%
ungroup() %>%
filter(temp != -99)
}
# Import NYNEWYOR.txt: my_data
my_data <- clean_weather(file="NYNEWYOR.txt")
# Create the stats object
StatHistorical <- ggproto("StatHistorical", Stat,
compute_group = function(data, scales, params) {
data <- data %>%
filter(year != max(year)) %>%
group_by(x) %>%
mutate(ymin = Hmisc::smean.cl.normal(y)[3],
ymax = Hmisc::smean.cl.normal(y)[2]) %>%
ungroup()
},
required_aes = c("x", "y", "year"))
# Create the layer
stat_historical <- function(mapping = NULL, data = NULL, geom = "point",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, ...) {
list(
layer(
stat = "identity", data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, col = "#EED8AE", alpha = 0.3, shape = 16, ...)
),
layer(
stat = StatHistorical, data = data, mapping = mapping, geom = "linerange",
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, col = "#8B7E66", ...)
)
)
}
# Build the plot
my_data <- clean_weather("NYNEWYOR.txt")
ggplot(my_data, aes(x = yearday, y = temp, year = year)) +
stat_historical()
# Create the stats object
StatPresent <- ggproto("StatPresent", Stat,
compute_group = function(data, scales, params) {
data <- filter(data, year == max(year))
},
required_aes = c("x", "y", "year")
)
# Create the layer
stat_present <- function(mapping = NULL, data = NULL, geom = "line",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, ...) {
layer(
stat = StatPresent, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
# Build the plot
my_data <- clean_weather("NYNEWYOR.txt")
ggplot(my_data, aes(x = yearday, y = temp, year = year)) +
stat_historical() +
stat_present()
# Create the stats object
StatExtremes <- ggproto("StatExtremes", Stat,
compute_group = function(data, scales, params) {
present <- data %>%
filter(year == max(year))
past <- data %>%
filter(year != max(year))
past_extremes <- past %>%
group_by(x) %>%
summarise(past_low = min(y),
past_high = max(y))
# transform data to contain extremes
data <- present %>%
left_join(past_extremes, by="x") %>%
mutate(record = ifelse(y < past_low,
"#0000CD",
ifelse(y > past_high,
"#CD2626",
"#00000000"
)
)
)
},
required_aes = c("x", "y", "year"))
# Create the layer
stat_extremes <- function(mapping = NULL, data = NULL, geom = "point",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, ...) {
layer(
stat = StatExtremes, data = data, mapping = mapping, geom = geom,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(na.rm = na.rm, ...)
)
}
# Build the plot
my_data <- clean_weather("NYNEWYOR.txt")
ggplot(my_data, aes(x = yearday, y = temp, year = year)) +
stat_historical() +
stat_present() +
stat_extremes(aes(col=..record..)) +
scale_color_identity()
# Data sets FRPARIS.txt, ILREYKJV.txt, UKLONDON.txt downloaded from International Cities at:
# http://academic.udayton.edu/kissock/http/Weather/citylistWorld.htm
# File paths of all datasets
my_files <- c("NYNEWYOR.txt","FRPARIS.txt", "ILREYKJV.txt", "UKLONDON.txt")
# Build my_data with a for loop
my_data <- NULL
for (file in my_files) {
temp <- clean_weather(file)
temp$id <- sub(".txt", "", file)
my_data <- rbind(my_data, temp)
}
# Build the final plot, from scratch!
ggplot(data=my_data, aes(x=yearday, y=temp, year=year)) +
stat_historical() +
stat_present() +
stat_extremes(aes(col=..record..)) +
scale_color_identity() +
facet_wrap(~id, ncol=2)
The ggvis is based on a “grammar of graphics” and is closely linked to ggplot2 (both designed by Wickham). The objective for ggvis is to combine the analytic power of R with the visual power of Javascript.
Broadly, the “grammar of graphics” includes several layers such as Data, Coordinate System, Marks, Properties, and the like. This is similar to ggplot2 though with a modified syntax that is more in line with dplyr chaining:
myData %>% ggvis(~myX, ~myY, fill = ~myFill, …) %>% layer_myMarkChoice()
Note that the := operator is the static assignment operator, ensuring that a call to “red” means the color “red” and not merely a character vector coerced to the required length with every entry being “red”. The ~ symbolizes that this is a variable in my dataset. So ~red would mean the variable red in the dataset undergoing plotting.
Some basic example code includes:
library(ggvis)
## Warning: package 'ggvis' was built under R version 3.2.5
##
## Attaching package: 'ggvis'
## The following object is masked from 'package:ggplot2':
##
## resolution
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# Change the code below to make a graph with red points
mtcars %>% ggvis(~wt, ~mpg, fill := "red") %>% layer_points()
# Change the code below draw smooths instead of points
mtcars %>% ggvis(~wt, ~mpg) %>% layer_smooths()
# Change the code below to make a graph containing both points and a smoothed summary line
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>% layer_smooths()
data(pressure)
str(pressure)
## 'data.frame': 19 obs. of 2 variables:
## $ temperature: num 0 20 40 60 80 100 120 140 160 180 ...
## $ pressure : num 0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ...
# Adapt the code: show bars instead of points
pressure %>% ggvis(~temperature, ~pressure) %>% layer_bars()
# Adapt the codee: show lines instead of points
pressure %>% ggvis(~temperature, ~pressure) %>% layer_lines()
# Extend the code: map the fill property to the temperature variable
pressure %>% ggvis(~temperature, ~pressure, fill=~temperature) %>% layer_points()
# Extend the code: map the size property to the pressure variable
pressure %>% ggvis(~temperature, ~pressure, size=~pressure) %>% layer_points()
There are three main new operators in ggvis (relative to ggplot):
The line is a special type of mark (second most common after points) - stroke, strokeWidth, strokeOpacity, strokeDash, fill, fillOpacity
Other forms include:
Example code includes:
data(faithful)
str(faithful)
## 'data.frame': 272 obs. of 2 variables:
## $ eruptions: num 3.6 1.8 3.33 2.28 4.53 ...
## $ waiting : num 79 54 74 62 85 55 88 85 51 85 ...
faithful %>% ggvis(~waiting, ~eruptions) %>% layer_points()
faithful %>%
ggvis(~waiting, ~eruptions, size = ~eruptions) %>%
layer_points(opacity := 0.5, fill := "blue", stroke := "black")
faithful %>%
ggvis(~waiting, ~eruptions, fillOpacity = ~eruptions) %>%
layer_points(size := 100, fill := "red", stroke := "red", shape := "cross")
data(pressure)
str(pressure)
## 'data.frame': 19 obs. of 2 variables:
## $ temperature: num 0 20 40 60 80 100 120 140 160 180 ...
## $ pressure : num 0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ...
# Modify this graph to map the size property to the pressure variable
pressure %>% ggvis(~temperature, ~pressure, size = ~pressure) %>% layer_points()
# Modify this graph by setting the size property
pressure %>% ggvis(~temperature, ~pressure, size := 100) %>% layer_points()
# Fix this code to set the fill property to red
pressure %>% ggvis(~temperature, ~pressure, fill := "red") %>% layer_points()
pressure %>%
ggvis(~temperature, ~pressure) %>%
layer_lines(stroke := "red", strokeWidth := 2, strokeDash := 6)
# texas %>% ggvis(~long, ~lat) %>% layer_paths(fill := "darkorange")
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
mtcars %>% compute_smooth(mpg ~ wt)
## pred_ resp_
## 1 1.513000 32.08897
## 2 1.562506 31.68786
## 3 1.612013 31.28163
## 4 1.661519 30.87037
## 5 1.711025 30.45419
## 6 1.760532 30.03318
## 7 1.810038 29.60745
## 8 1.859544 29.17711
## 9 1.909051 28.74224
## 10 1.958557 28.30017
## 11 2.008063 27.83462
## 12 2.057570 27.34766
## 13 2.107076 26.84498
## 14 2.156582 26.33229
## 15 2.206089 25.81529
## 16 2.255595 25.29968
## 17 2.305101 24.79115
## 18 2.354608 24.29542
## 19 2.404114 23.81818
## 20 2.453620 23.36514
## 21 2.503127 22.95525
## 22 2.552633 22.61385
## 23 2.602139 22.32759
## 24 2.651646 22.08176
## 25 2.701152 21.86167
## 26 2.750658 21.65260
## 27 2.800165 21.43987
## 28 2.849671 21.20875
## 29 2.899177 20.95334
## 30 2.948684 20.71584
## 31 2.998190 20.49571
## 32 3.047696 20.28293
## 33 3.097203 20.06753
## 34 3.146709 19.83950
## 35 3.196215 19.58885
## 36 3.245722 19.29716
## 37 3.295228 18.94441
## 38 3.344734 18.56700
## 39 3.394241 18.20570
## 40 3.443747 17.90090
## 41 3.493253 17.62060
## 42 3.542759 17.34002
## 43 3.592266 17.07908
## 44 3.641772 16.81759
## 45 3.691278 16.55757
## 46 3.740785 16.30833
## 47 3.790291 16.07916
## 48 3.839797 15.87937
## 49 3.889304 15.70181
## 50 3.938810 15.52594
## 51 3.988316 15.35173
## 52 4.037823 15.17933
## 53 4.087329 15.00894
## 54 4.136835 14.84072
## 55 4.186342 14.67484
## 56 4.235848 14.51148
## 57 4.285354 14.35082
## 58 4.334861 14.19302
## 59 4.384367 14.03826
## 60 4.433873 13.88672
## 61 4.483380 13.73856
## 62 4.532886 13.59396
## 63 4.582392 13.45310
## 64 4.631899 13.31614
## 65 4.681405 13.18326
## 66 4.730911 13.05464
## 67 4.780418 12.93045
## 68 4.829924 12.81086
## 69 4.879430 12.69604
## 70 4.928937 12.58617
## 71 4.978443 12.48143
## 72 5.027949 12.38198
## 73 5.077456 12.28799
## 74 5.126962 12.19966
## 75 5.176468 12.11713
## 76 5.225975 12.04060
## 77 5.275481 11.97023
## 78 5.324987 11.90620
## 79 5.374494 11.84868
## 80 5.424000 11.79784
# Extend with ggvis() and layer_lines()
mtcars %>% compute_smooth(mpg ~ wt) %>% ggvis(~pred_, ~resp_) %>% layer_lines()
# Extend with layer_points() and layer_smooths()
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>% layer_smooths()
Behind the scenes, ggvis uses several compute functions to help with visualizations:
The ggvis library is especially well designed to interact with the dplyr library (Hadley Wickham):
Example code includes:
data(faithful)
str(faithful)
## 'data.frame': 272 obs. of 2 variables:
## $ eruptions: num 3.6 1.8 3.33 2.28 4.53 ...
## $ waiting : num 79 54 74 62 85 55 88 85 51 85 ...
faithful %>% ggvis(~waiting) %>% layer_histograms(width = 5)
# Finish the command
faithful %>%
compute_bin(~waiting, width = 5) %>%
ggvis(x = ~xmin_, x2 = ~xmax_, y = 0, y2 = ~count_) %>%
layer_rects()
# Build the density plot
faithful %>% ggvis(~waiting, fill := "green") %>% layer_densities()
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
mtcars %>%
ggvis(x = ~factor(cyl)) %>%
layer_bars()
# Instruction 1
mtcars %>%
group_by(cyl) %>%
ggvis(~mpg, ~wt, stroke = ~factor(cyl)) %>%
layer_smooths()
## Warning in rbind_all(out[[1]]): Unequal factor levels: coercing to
## character
# Instruction 2
mtcars %>%
group_by(cyl) %>%
ggvis(~mpg, fill = ~factor(cyl)) %>%
layer_densities()
## Warning in rbind_all(out[[1]]): Unequal factor levels: coercing to
## character
mtcars %>%
group_by(cyl, am) %>%
ggvis(~mpg, fill = ~interaction(cyl, am)) %>%
layer_densities()
## Warning in rbind_all(out[[1]]): Unequal factor levels: coercing to
## character
Can add interactivity to plots in ggvis:
Multi-layered ggvis plots:
Example code includes:
data(faithful)
str(faithful)
## 'data.frame': 272 obs. of 2 variables:
## $ eruptions: num 3.6 1.8 3.33 2.28 4.53 ...
## $ waiting : num 79 54 74 62 85 55 88 85 51 85 ...
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# Adapt the code: set fill with a select box
faithful %>%
ggvis(~waiting, ~eruptions, fillOpacity := 0.5,
shape := input_select(label = "Choose shape:",
choices = c("circle", "square", "cross",
"diamond", "triangle-up", "triangle-down"
)
),
fill := input_select(label = "Choose color:",
choices = c("black", "red", "blue", "green")
)
) %>%
layer_points()
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.
# Add radio buttons to control the fill of the plot
mtcars %>%
ggvis(~mpg, ~wt,
fill := input_radiobuttons(label = "Choose color:",
choices = c("black", "red", "blue", "green")
)
) %>%
layer_points()
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.
mtcars %>%
ggvis(~mpg, ~wt,
fill := input_text(label = "Choose color:", value = "black")) %>%
layer_points()
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.
# Map the fill property to a select box that returns variable names
mtcars %>%
ggvis(~mpg, ~wt, fill = input_select(label = "Choose fill variable:",
choices = names(mtcars), map=as.name
)
) %>%
layer_points()
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.
# Map the bindwidth to a numeric field ("Choose a binwidth:")
mtcars %>%
ggvis(~mpg) %>%
layer_histograms(width = input_numeric(label = "Choose a binwidth:", value = 1))
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.
# Map the binwidth to a slider bar ("Choose a binwidth:") with the correct specifications
mtcars %>%
ggvis(~mpg) %>%
layer_histograms(width = input_slider(label = "Choose a binwidth:", 1, 20))
## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.
# Add a layer of points to the graph below.
pressure %>%
ggvis(~temperature, ~pressure, stroke := "skyblue") %>%
layer_lines() %>%
layer_points()
# Copy and adapt so that only the lines layer uses a skyblue stroke.
pressure %>%
ggvis(~temperature, ~pressure) %>%
layer_lines(stroke := "skyblue") %>%
layer_points()
# Rewrite the code below so that only the points layer uses the shape property.
pressure %>%
ggvis(~temperature, ~pressure) %>%
layer_lines(stroke := "skyblue") %>%
layer_points(shape := "triangle-up")
# Refactor the code for the graph below to make it as concise as possible
pressure %>%
ggvis(~temperature, ~pressure, stroke := "skyblue", strokeOpacity := 0.5, strokeWidth := 5) %>%
layer_lines() %>%
layer_points(fill = ~temperature,
shape := "triangle-up",
size := 300)
# Add more layers to the line plot
pressure %>%
ggvis(~temperature, ~pressure) %>%
layer_lines(opacity := 0.5) %>%
layer_points() %>%
layer_model_predictions(model = "lm", stroke := "navy") %>%
layer_smooths(stroke := "skyblue")
## Guessing formula = pressure ~ temperature
The add_axis() function can be used to change the titles and axis labels:
The add_legends() function can help with cleaning up legends (make them look tidier). This is similar to the arguments passed to the “adding an axis” above.
Can also customize the scales (relationships between data spaces and visual spaces) for the data:
Example code includes:
data(faithful)
str(faithful)
## 'data.frame': 272 obs. of 2 variables:
## $ eruptions: num 3.6 1.8 3.33 2.28 4.53 ...
## $ waiting : num 79 54 74 62 85 55 88 85 51 85 ...
# Defaulted axis
faithful %>%
ggvis(~waiting, ~eruptions) %>%
layer_points()
# Customized axis
faithful %>%
ggvis(~waiting, ~eruptions) %>%
layer_points() %>%
add_axis("x", title="Time since previous eruption (m)",
values=c(50, 60, 70, 80, 90), subdivide=9, orient="top"
) %>%
add_axis("y", title="Duration of eruption (m)", values=c(2, 3, 4, 5),
subdivide=9, orient="right"
)
data(pressure)
str(pressure)
## 'data.frame': 19 obs. of 2 variables:
## $ temperature: num 0 20 40 60 80 100 120 140 160 180 ...
## $ pressure : num 0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ...
# Add a legend
faithful %>%
ggvis(~waiting, ~eruptions, opacity := 0.6,
fill = ~factor(round(eruptions))) %>%
layer_points() %>%
add_legend("fill", title="~ duration (m)", orient="left")
# Original code with jumbled legends
faithful %>%
ggvis(~waiting, ~eruptions, opacity := 0.6,
fill = ~factor(round(eruptions)), shape = ~factor(round(eruptions)),
size = ~round(eruptions)) %>%
layer_points()
# Fix the legend
faithful %>%
ggvis(~waiting, ~eruptions, opacity := 0.6,
fill = ~factor(round(eruptions)), shape = ~factor(round(eruptions)),
size = ~round(eruptions)) %>%
layer_points() %>%
add_legend(c("fill", "shape", "size"), title="~ duration (m)")
data(mtcars)
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
# Add a scale_numeric()
mtcars %>%
ggvis(~wt, ~mpg, fill = ~disp, stroke = ~disp, strokeWidth := 2) %>%
layer_points() %>%
scale_numeric("fill", range = c("red", "yellow")) %>%
scale_numeric("stroke", range = c("darkred", "orange"))
# Add a scale_numeric()
mtcars %>% ggvis(~wt, ~mpg, fill = ~hp) %>%
layer_points() %>%
scale_numeric("fill", range=c("green", "beige"))
# Add a scale_nominal()
mtcars %>% ggvis(~wt, ~mpg, fill = ~factor(cyl)) %>%
layer_points() %>%
scale_nominal("fill", range=c("purple", "blue", "green"))
# Original plot becomes too transparent
mtcars %>% ggvis(x = ~wt, y = ~mpg, fill = ~factor(cyl), opacity = ~hp) %>%
layer_points()
# Range to prevent overly transparent data points
mtcars %>% ggvis(x = ~wt, y = ~mpg, fill = ~factor(cyl), opacity = ~hp) %>%
layer_points() %>%
scale_numeric("opacity", range=c(0.2, 1))
mtcars %>% ggvis(~wt, ~mpg, fill = ~disp) %>%
layer_points() %>%
scale_numeric("y", domain = c(0, NA)) %>% # NA means top-of-data-range
scale_numeric("x", domain = c(0, 6))
mtcars$color <- c('red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan' ,
'red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan' ,
'red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan' ,
'red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan'
)
# Using fill by mapping the "color" variable to the ggvis scales
mtcars %>%
ggvis(x = ~wt, y = ~mpg, fill = ~color) %>%
layer_points()
# Using fill based directly on the values in the "color" variable
mtcars %>%
ggvis(x = ~wt, y = ~mpg, fill := ~color) %>%
layer_points()